Cloud deployment of a data fabric service system

ABSTRACT

The performance and flexibility of a data intake and query system having capabilities extended by a fabric service (DFS) system can be improved with deployment on a cloud computing platform. The DFS system can extend the capabilities of a data intake and query system by leveraging computing assets from anywhere in a big data ecosystem to collectively execute search queries on diverse data systems regardless of whether data stores are internal of the data intake and query system and/or external data stores that are communicatively coupled to the data intake and query system over a network.

CROSS-REFERENCE OF RELATED APPLICATIONS

The present application is a Continuation of U.S. patent applicationSer. No. 15/276,717, filed on Sep. 26, 2016, entitled “DATA FABRICSERVICE SYSTEM ARCHITECTURE”, which is hereby incorporated by referencein its entirety.

COPYRIGHT NOTICE

A portion of the disclosure of this patent document contains materialwhich is subject to copyright protection. The copyright owner has noobjection to the facsimile reproduction by anyone of the patent documentor the patent disclosure, as it appears in the Patent and TrademarkOffice patent file or records, but otherwise reserves all copyrightrights whatsoever.

FIELD

At least one embodiment of the present disclosure pertains to a datafabric service system architecture and, more particularly, a data fabricservice system architecture for searching and analyzing data ofdistributed data systems.

BACKGROUND

Information technology (IT) environments can include diverse types ofdata systems that store large amounts of diverse data types generated bynumerous devices. For example, a big data ecosystem may includedatabases such as MySQL and Oracle databases, cloud computing servicessuch as Amazon web services (AWS), and other data systems that storepassively or actively generated data, including machine-generated data(“machine data”). The machine data can include performance data,diagnostic data, or any other data that can be analyzed to diagnoseequipment performance problems, monitor user interactions, and to deriveother insights.

The large amount and diversity of data systems containing large amountsof structured, semi-structured, and unstructured data relevant to anysearch query can be massive, and continues to grow rapidly. Thistechnological evolution can give rise to various challenges in relationto managing, understanding and effectively utilizing the data. To reducethe potentially vast amount of data that may be generated, some datasystems pre-process data based on anticipated data analysis needs. Inparticular, specified data items may be extracted from the generateddata and stored in a data system to facilitate efficient retrieval andanalysis of those data items at a later time. At least some of theremainder of the generated data is typically discarded duringpre-processing.

However, storing massive quantities of minimally processed orunprocessed data (collectively and individually referred to as “rawdata”) for later retrieval and analysis is becoming increasingly morefeasible as storage capacity becomes more inexpensive and plentiful. Ingeneral, storing raw data and performing analysis on that data later canprovide greater flexibility because it enables an analyst to analyze allof the generated data instead of only a fraction of it.

Although the availability of vastly greater amounts of diverse data ondiverse data systems provides opportunities to derive new insights, italso gives rise to technical challenges to search and analyze the data.Tools exist that allow an analyst to search data systems separately andcollect search results over a network for the analyst to derive insightsin a piecemeal manner. However, tools that allow analysts to seamlesslysearch and analyze diverse data types of diverse data systems do notexist.

BRIEF DESCRIPTION OF THE DRAWINGS

One or more embodiments of the present disclosure are illustrated by wayof example and not limitation in the figures of the accompanyingdrawings, in which like references indicate similar elements.

FIG. 1 is a high-level system diagram in which an embodiment may beimplemented;

FIG. 2 is a block diagram illustrating a series of events including rawdata according to some embodiments of the present disclosure;

FIG. 3 illustrates a networked computer environment in which anembodiment may be implemented;

FIG. 4 illustrates a block diagram of an example data intake and querysystem in which an embodiment may be implemented;

FIG. 5 is a flow diagram that illustrates how indexers process, index,and store data received from forwarders according to some embodiments ofthe present disclosure;

FIG. 6 is a flow diagram that illustrates how a search head and indexersperform a search query according to some embodiments of the presentdisclosure;

FIG. 7 illustrates a scenario where a common customer ID is found amonglog data received from three disparate sources according to someembodiments of the present disclosure;

FIG. 8A illustrates a search screen according to some embodiments of thepresent disclosure;

FIG. 8B illustrates a data summary dialog that enables a user to selectvarious data sources according to some embodiments of the presentdisclosure;

FIG. 9A illustrates a user interface screen for an example datamodel-driven report generation interface according to some embodimentsof the present disclosure;

FIG. 9B illustrates a user interface screen for an example datamodel-driven report generation interface according to some embodimentsof the present disclosure;

FIG. 9C illustrates a user interface screen for an example datamodel-driven report generation interface according to some embodimentsof the present disclosure;

FIG. 9D illustrates a user interface screen for an example datamodel-driven report generation interface according to some embodimentsof the present disclosure;

FIG. 10 illustrates an example search query received from a client andexecuted by search peers according to some embodiments of the presentdisclosure;

FIG. 11A illustrates a key indicators view according to some embodimentsof the present disclosure;

FIG. 11B illustrates an incident review dashboard according to someembodiments of the present disclosure;

FIG. 11C illustrates a proactive monitoring tree according to someembodiments of the present disclosure;

FIG. 11D illustrates a user interface screen displaying both log dataand performance data according to some embodiments of the presentdisclosure;

FIG. 12 illustrates a block diagram of an example cloud-based dataintake and query system in which an embodiment may be implemented;

FIG. 13 illustrates a block diagram of an example data intake and querysystem that performs searches across external data systems according tosome embodiments of the present disclosure;

FIG. 14 illustrates a user interface screen for an example datamodel-driven report generation interface according to some embodimentsof the present disclosure;

FIG. 15 illustrates a user interface screen for an example datamodel-driven report generation interface according to some embodimentsof the present disclosure;

FIG. 16 illustrates a user interface screen for an example datamodel-driven report generation interface according to some embodimentsof the present disclosure;

FIG. 17 illustrates example visualizations generated by a reportingapplication according to some embodiments of the present disclosure;

FIG. 18 illustrates example visualizations generated by a reportingapplication according to some embodiments of the present disclosure;

FIG. 19 illustrates example visualizations generated by a reportingapplication according to some embodiments of the present disclosure;

FIG. 20 is a system diagram illustrating a data fabric service systemarchitecture (“DFS system”) in which an embodiment may be implemented;

FIG. 21 is an operation flow diagram illustrating an example of anoperation flow of a DFS system according to some embodiments of thepresent disclosure;

FIG. 22 is an operation flow diagram illustrating an example of aparallel export operation performed in a DFS system according to someembodiments of the present disclosure;

FIG. 23 is a flow diagram illustrating a method performed by the DFSsystem to obtain time-ordered search results according to someembodiments of the present disclosure;

FIG. 24 is a flow diagram illustrating a method performed by a dataintake and query system of a DFS system to obtain time-ordered searchresults according to some embodiments of the present disclosure;

FIG. 25 is a flow diagram illustrating a method performed by nodes of aDFS system to obtain batch or reporting search results according to someembodiments of the present disclosure;

FIG. 26 is a flow diagram illustrating a method performed by a dataintake and query system of a DFS system in response to a reportingsearch query according to some embodiments of the present disclosure;

FIG. 27 is a system diagram illustrating a co-located deployment of aDFS system in which an embodiment may be implemented;

FIG. 28 is an operation flow diagram illustrating an example of anoperation flow of a co-located deployment of a DFS system according tosome embodiments of the present disclosure;

FIG. 29 is a cloud based system diagram illustrating a cloud deploymentof a DFS system in which an embodiment may be implemented;

FIG. 30 is a flow diagram illustrating an example of a method performedin a cloud-based DFS system according to some embodiments of the presentdisclosure;

FIG. 31 is a flow diagram illustrating a timeline mechanism thatsupports rendering search results in a time-ordered visualizationaccording to some embodiments of the present disclosure;

FIG. 32 illustrates a timeline visualization rendered on a GUI in whichan embodiment may be implemented;

FIG. 33 illustrates a selected bin of a timeline visualization and thecontents of the selected bin according to some embodiments of thepresent disclosure.

FIG. 34 is a flow diagram illustrating services of a DFS systemaccording to some embodiments of the present disclosure; and

FIG. 35 is a block diagram illustrating a high-level example of ahardware architecture of a computing system in which an embodiment maybe implemented.

DETAILED DESCRIPTION

In this description, references to “an embodiment,” “one embodiment,” orthe like, mean that the particular feature, function, structure orcharacteristic being described is included in at least one embodiment ofthe technique introduced herein. Occurrences of such phrases in thisspecification do not necessarily all refer to the same embodiment. Onthe other hand, the embodiments referred to are also not necessarilymutually exclusive.

A data intake and query system can index and store data in data storesof indexers, and can receive search queries causing a search of theindexers to obtain search results. The data intake and query systemtypically has search, extraction, execution, and analytics capabilitiesthat are limited in scope to the data stores of the indexers (“internaldata stores”). Hence, a seamless and comprehensive search and analysisthat includes diverse data types from external data sources is notpossible. Thus, the capabilities of data intake and query systems remainisolated from external data sources that could improve search results toprovide new insights.

The disclosed embodiments overcome these drawbacks by extending thesearch and analytics capabilities of a data intake and query system toinclude diverse data types stored in diverse data systems external fromthe data intake and query system. As a result, an analyst can use thedata intake and query system to search and analyze internal data storesand/or external data systems including enterprise systems and opensource technologies of a big data ecosystem. The term “big data” refersto large data sets that may be analyzed computationally to revealpatterns, trends, and associations, in some cases, relating to humanbehavior and interactions.

In particular, introduced herein is a data fabric service systemarchitecture (“DFS system”) that has the ability to execute big dataanalytics seamlessly and can scale across diverse data sources to enableprocessing large volumes of diverse data from diverse data systems. A“data source” can include a “data system,” which may refer to a systemthat can process and/or store data. A “data storage system” may refer toa storage system that can store data such as unstructured,semi-structured, or structured data. Accordingly, a data source caninclude a data system that includes a data storage system.

The DFS system can improve search and analytics capabilities of a dataintake and query system by employing services combined with a scalablenetwork of distributed nodes communicatively coupled to diverse datasystems. The network of distributed nodes can act as agents of the dataintake and query system to collect and process data of distributed datasystems, and the services can provide the processed data to the dataintake and query system as search results.

For example, the data intake and query system can respond to a searchquery by executing search operations on internal and external datasources to obtain partial search results that are harmonized andpresented as search results of the search query. As such, the dataintake and query system can offload search and analytics operations tothe distributed nodes. Hence, the DFS architecture enables search andanalytics capabilities that can extend beyond the data intake and querysystem to include external data systems.

The DFS system can provide big data open stack integration to act as abig data pipeline that extends the search and analytics capabilities ofa data intake and query system over numerous and diverse external dataresources. For example, the DFS system can extend the data executionscope of the data intake and query system to include data residing inexternal data systems such as MySQL, PostgreSQL, and Oracle databases;NoSQL data stores like Cassandra, Mongo DB; and cloud storage likeAmazon S3 and Hadoop distributed file system (HDFS). Thus, the DFSsystem can execute search and analytics operations for all possiblecombinations of data types on internal data stores, external datastores, or a combination of both.

The distributed processing of the DFS system enables scalability toinclude any number of distributed data systems. As such, search queriesreceived by the data intake and query system can be propagated to thenetwork of distributed nodes to extend the search and analyticscapabilities of the data intake and query system over external datasystems. In this context, the network of distributed nodes can act as anextension of the local data intake in query system's data processingpipeline to facilitate scalable analytics across the diverse datasystems. Accordingly, the DFS system can extend and transform the dataintake and query system to include data resources into a data fabricplatform that can leverage computing assets from anywhere and access andexecute on data regardless of type or origin.

The disclosed embodiments include services such as new searchcapabilities, visualization tools, and other services that areseamlessly integrated into the DFS system. For example, the disclosedtechniques include new search services performed on internal datastores, external data stores, or a combination of both. The searchoperations can provide ordered or unordered search results, or searchresults derived from data of diverse data systems, which can bevisualized to provide new and useful insights about the data containedin a big data ecosystem.

Various other features of the DFS system introduced here will becomeapparent from the description that follows. First, however, it is usefulto consider an example of an environment and system in which thetechniques can be employed, as will now be described.

1.0. General Overview

The embodiments disclosed herein generally refer to a data fabricservice system architecture (“DFS system”) that includes a data intakeand query system, services, a network of distributed nodes, anddistributed data systems, all interconnected over one or more networks.However, embodiments of the disclosed system can include many computingcomponents including software, servers, routers, client devices, andhost devices that are not specifically described herein. As used herein,a “node” can refer to one or more devices and/or software running ondevices that enable the devices to provide services of the DFS system.For example, a node can include devices running software that enableexecution of a DFS search service.

FIG. 1 is a high-level system diagram in which an embodiment may beimplemented. The system 10 includes distributed external data systems12-1 and 12-2 (also referred to collectively and individually asexternal data system(s) 12). The external data systems 12 arecommunicatively coupled to worker nodes 14-1 and 14-2, respectively(also referred to collectively and individually as worker node(s) 14).The system 10 also includes a data intake and query system 16interconnected to the worker nodes 14 over network 18. The system 10 canalso include a client device 22 and applications running on the clientdevice 22. An example includes a personal computer running a networkbrowser application that enables a user of the client device 22 toaccess any of the data systems.

The data intake and query system 16 and the external data systems 12 caneach store data obtained from various data sources. For example, thedata intake and query system 16 can store data in internal data stores20 (also referred to as an internal storage system), and the externaldata systems 12 can store data in respective external data stores 22(also referred to as external storage systems). However, the data intakeand query system 16 and external data systems 12 may process and storedata differently. For example, as explained in greater detail below, thedata intake and query system 16 may store minimally processed orunprocessed data (“raw data”). In contrast, the external data systems 12may store pre-processed data rather than raw data. Hence, the dataintake and query system 16 and the external data systems 12 can operateindependent of each other in a big data ecosystem.

The worker nodes 14 can act as agents of the data intake and querysystem 16 to process data collected from the internal data stores 20 andthe external data stores 22. The worker nodes 14 may reside on one ormore computing devices such as servers communicatively coupled to theexternal data systems 12. The processed data can then be returned to thedata intake and query system 16. As such, the worker nodes 14 can extendthe search and analytics capabilities of the data intake and querysystem 16 to act on diverse data systems.

The external data systems 12 may include one or more computing devicesthat can store structured, semi-structured, or unstructured data. Eachexternal data system 12 can generate and/or collect generated data, andstore the generated data in their respective external data stores 22.For example, the external data system 12-1 may include a server runninga MySQL database that stores structured data objects such astime-stamped events, and the external data system 12-2 may be a serverof cloud computing services such as Amazon web services (AWS) that canprovide different data types ranging from unstructured (e.g., s3) tostructured (e.g., redshift).

The external data systems 12 are said to be external to the data intakeand query system 16 because the data stored at the external data stores22 has not necessarily been processed or passed through the data intakeand query system 16. In other words, the data intake and query system 16may have no control or influence over how data is processed, controlled,or managed by the external data systems 12.

The external data systems 12 can process data, perform requests receivedfrom other computing systems, and perform numerous other computationaltasks independent of each other and independent of the data intake andquery system 16. For example, the external data system 12-1 may be aserver that can process data locally that reflects correlations amongthe stored data. The external data systems 12 may generate and/or storeever increasing volumes of data without any interaction with the dataintake and query system 16. As such, each of the external data system 12may act independently to control, manage, and process the data theycontain.

Data stored in the internal data stores 20 and external data stores 22may be related. For example, an online transaction could generatevarious forms of data stored in disparate locations and in variousformats. The generated data may include payment information, customerinformation, and information about suppliers, retailers, and the like.Other examples of data generated in a big data ecosystem includeapplication program data, system logs, network packet data, error logs,stack traces, and performance data. The data can also include diagnosticinformation and many other types of data that can be analyzed to performlocal actions, diagnose performance problems, monitor interactions, andderive other insights.

The volume of generated data can grow at very high rates as the numberof transactions and diverse data systems grows. A portion of this largevolume of data could be processed and stored by the data intake andquery system 16 while other portions could be stored in any of theexternal data systems 12. In an effort to reduce the vast amounts of rawdata generated in a big data ecosystem, some of the external datasystems 12 may pre-process the raw data based on anticipated dataanalysis needs, store the pre-processed data, and discard any remainingraw data. However, discarding massive amounts of raw data can result inthe loss of valuable insights that could have been obtained by searchingall of the raw data.

In contrast, the data intake and query system 16 can address some ofthese challenges by collecting and storing raw data as structured“events.” FIG. 2 is a block diagram illustrating a series of eventsincluding raw edge data according to some embodiments of the presentdisclosure. An event includes a portion of raw data and is associatedwith a specific point in time. For example, events may be derived from“time series data,” where the time series data comprises a sequence ofdata points (e.g., performance measurements from a computer system) thatare associated with successive points in time.

As shown, each event 1 through K can be associated with a timestamp 1through K that can be derived from the raw data in the respective event,determined through interpolation between temporally proximate eventshaving known timestamps, or determined based on other configurable rulesfor associating timestamps with events. During operation of the dataintake and query system 16, ingested raw data is divided into segmentsof raw data delineated by time segments (e.g., blocks of raw data, eachassociated with a specific time frame). The segments of raw data areindexed as timestamped events, corresponding to their respective timesegments as shown in FIG. 2. The system stores the timestamped events ina data store.

In some instances, data systems can store raw data in a predefinedformat, where data items with specific data formats are stored atpredefined locations in the data. For example, the raw data may includedata stored as fields. In other instances, raw data may not have apredefined format; that is, the data is not at fixed, predefinedlocations, but the data does have repeatable patterns and is not random.This means that some raw data can comprise various data items ofdifferent data types that may be stored at different locations withinthe raw data. As shown in FIG. 2, each event 1 through K includes afield that is nine characters in length beginning after a semicolon on afirst line of the raw edge data, for example. In certain embodiments,these fields can be queried to extract their contents.

In some embodiments, the external data systems 12 can store raw data asevents that are indexed by timestamps but are also associated withpredetermined data items. This structure is essentially a modificationof conventional database systems that require predetermining data itemsfor subsequent searches. These systems can be modified to retain theremaining raw data for subsequent re-processing for other predetermineddata items.

Specifically, the raw data can be divided into segments and indexed bytimestamps. The predetermined data items can be associated with theevents indexed by timestamps. The events can be searched only for thepredetermined data items during search time; the events can bere-processed later in time to re-index the raw data, and generate eventswith new predetermined data items. As such, the data systems of thesystem 10 can store related data in a variety of pre-processed data andraw data in a variety of structures.

A number of tools are available to search and analyze data contained inthese diverse data systems. As such, an analyst can use a tool to searcha database of the external data system 12-1. A different tool could beused to search a cloud services application of the external data system12-2. Yet another different tool could be used to search the internaldata stores 20. Moreover, different tools can perform analytics of datastored in proprietary or open source data stores. However, existingtools cannot obtain valuable insights from data contained in acombination of the data intake and query system 16 and/or any of theexternal data systems 12. Examples of these valuable insights mayinclude correlations between the structured data of the external datastores 22 and raw data of the internal data stores 20.

The disclosed techniques can extend the search, extraction, execution,and analytics capabilities of data intake and query systems toseamlessly search and analyze multiple diverse data of diverse datasystems in a big data ecosystem. The disclosed techniques can transforma big data ecosystem into a big data pipeline between external datasystems and a data intake and query system, to enable seamless searchand analytics operations on a variety of data sources, which can lead tonew insights that were not previously available. Hence, the disclosedtechniques include a data intake and query system extended to searchexternal data systems into a data fabric platform that can leveragecomputing assets from anywhere and access and execute on data regardlessof type and origin.

2.0. Overview of Data Intake and Query Systems

As indicated above, modern data centers and other computing environmentscan comprise anywhere from a few host computer systems to thousands ofsystems configured to process data, service requests from remoteclients, and perform numerous other computational tasks. Duringoperation, various components within these computing environments oftengenerate significant volumes of machine-generated data. For example,machine data is generated by various components in the informationtechnology (IT) environments, such as servers, sensors, routers, mobiledevices, Internet of Things (IoT) devices, etc. Machine-generated datacan include system logs, network packet data, sensor data, applicationprogram data, error logs, stack traces, system performance data, etc. Ingeneral, machine-generated data can also include performance data,diagnostic information, and many other types of data that can beanalyzed to diagnose performance problems, monitor user interactions,and to derive other insights.

A number of tools are available to analyze machine data, that is,machine-generated data. In order to reduce the size of the potentiallyvast amount of machine data that may be generated, many of these toolstypically pre-process the data based on anticipated data-analysis needs.For example, pre-specified data items may be extracted from the machinedata and stored in a database to facilitate efficient retrieval andanalysis of those data items at search time. However, the rest of themachine data typically is not saved and discarded during pre-processing.As storage capacity becomes progressively cheaper and more plentiful,there are fewer incentives to discard these portions of machine data andmany reasons to retain more of the data.

This plentiful storage capacity is presently making it feasible to storemassive quantities of minimally processed machine data for laterretrieval and analysis. In general, storing minimally processed machinedata and performing analysis operations at search time can providegreater flexibility because it enables an analyst to search all of themachine data, instead of searching only a pre-specified set of dataitems. This may enable an analyst to investigate different aspects ofthe machine data that previously were unavailable for analysis.

However, analyzing and searching massive quantities of machine datapresents a number of challenges. For example, a data center, servers, ornetwork appliances may generate many different types and formats ofmachine data (e.g., system logs, network packet data (e.g., wire data,etc.), sensor data, application program data, error logs, stack traces,system performance data, operating system data, virtualization data,etc.) from thousands of different components, which can collectively bevery time-consuming to analyze. In another example, mobile devices maygenerate large amounts of information relating to data accesses,application performance, operating system performance, networkperformance, etc. There can be millions of mobile devices that reportthese types of information.

These challenges can be addressed by using an event-based data intakeand query system, such as the SPLUNK® ENTERPRISE system developed bySplunk Inc. of San Francisco, Calif. The SPLUNK® ENTERPRISE system isthe leading platform for providing real-time operational intelligencethat enables organizations to collect, index, and searchmachine-generated data from various websites, applications, servers,networks, and mobile devices that power their businesses. The SPLUNK®ENTERPRISE system is particularly useful for analyzing data which iscommonly found in system log files, network data, and other data inputsources. Although many of the techniques described herein are explainedwith reference to a data intake and query system similar to the SPLUNK®ENTERPRISE system, these techniques are also applicable to other typesof data systems.

In the SPLUNK® ENTERPRISE system, machine-generated data are collectedand stored as “events” such as those shown in FIG. 2. Hence, an eventcomprises a portion of the machine-generated data and is associated witha specific point in time. For example, events may be derived from “timeseries data,” where the time series data comprises a sequence of datapoints (e.g., performance measurements from a computer system, etc.)that are associated with successive points in time. In general, eachevent can be associated with a timestamp that is derived from the rawdata in the event, determined through interpolation between temporallyproximate events having known timestamps, or determined based on otherconfigurable rules for associating timestamps with events, etc.

In some instances, machine data can have a predefined format, where dataitems with specific data formats are stored at predefined locations inthe data. For example, the machine data may include data stored asfields in a database table. In other instances, machine data may nothave a predefined format, that is, the data is not at fixed, predefinedlocations, but the data does have repeatable patterns and is not random.This means that some machine data can comprise various data items ofdifferent data types and may be stored at different locations within thedata. For example, when the data source is an operating system log, anevent can include one or more lines from the operating system logcontaining raw data that includes different types of performance anddiagnostic information associated with a specific point in time.

Examples of components which may generate machine data from which eventscan be derived include, but are not limited to, web servers, applicationservers, databases, firewalls, routers, operating systems, and softwareapplications that execute on computer systems, mobile devices, sensors,Internet of Things (IoT) devices, etc. The data generated by such datasources can include, for example and without limitation, server logfiles, activity log files, configuration files, messages, network packetdata, performance measurements, sensor measurements, etc.

The SPLUNK® ENTERPRISE system uses flexible schema to specify how toextract information from the event data. A flexible schema may bedeveloped and redefined as needed. Note that a flexible schema may beapplied to event data “on the fly,” when it is needed (e.g., at searchtime, index time, ingestion time, etc.). When the schema is not appliedto event data until search time it may be referred to as a “late-bindingschema.”

During operation, the SPLUNK® ENTERPRISE system starts with raw inputdata (e.g., one or more system logs, streams of network packet data,sensor data, application program data, error logs, stack traces, systemperformance data, etc.). The system divides this raw data into blocks(e.g., buckets of data, each associated with a specific time frame,etc.), and parses the raw data to produce timestamped events. The systemstores the timestamped events in a data store. The system enables usersto run queries against the stored data to, for example, retrieve eventsthat meet criteria specified in a query, such as containing certainkeywords or having specific values in defined fields. As used hereinthroughout, data that is part of an event is referred to as “eventdata.” In this context, the term “field” refers to a location in theevent data containing one or more values for a specific data item. Aswill be described in more detail herein, the fields are defined byextraction rules (e.g., regular expressions) that derive one or morevalues from the portion of raw machine data in each event that has aparticular field specified by an extraction rule. The set of values soproduced are semantically related (such as an IP address), even thoughthe raw machine data in each event may be in different formats (e.g.,semantically related values may be in different positions in the eventsderived from different sources).

As noted above, the SPLUNK® ENTERPRISE system utilizes a late-bindingschema to event data while performing queries on events. One aspect of alate-binding schema is applying “extraction rules” to event data toextract values for specific fields during search time. Morespecifically, the extraction rules for a field can include one or moreinstructions that specify how to extract a value for the field from theevent data. An extraction rule can generally include any type ofinstruction for extracting values from data in events. In some cases, anextraction rule comprises a regular expression where a sequence ofcharacters form a search pattern, in which case the rule is referred toas a “regex rule.” The system applies the regex rule to the event datato extract values for associated fields in the event data by searchingthe event data for the sequence of characters defined in the regex rule.

In the SPLUNK® ENTERPRISE system, a field extractor may be configured toautomatically generate extraction rules for certain field values in theevents when the events are being created, indexed, or stored, possiblyat a later time. Alternatively, a user may manually define extractionrules for fields using a variety of techniques. In contrast to aconventional schema for a database system, a late-binding schema is notdefined at data ingestion time. Instead, the late-binding schema can bedeveloped on an ongoing basis until the time a query is actuallyexecuted. This means that extraction rules for the fields in a query maybe provided in the query itself, or may be located during execution ofthe query. Hence, as a user learns more about the data in the events,the user can continue to refine the late-binding schema by adding newfields, deleting fields, or modifying the field extraction rules for usethe next time the schema is used by the system. Because the SPLUNK®ENTERPRISE system maintains the underlying raw data and useslate-binding schema for searching the raw data, it enables a user tocontinue investigating and learn valuable insights about the raw data.

In some embodiments, a common field name may be used to reference two ormore fields containing equivalent data items, even though the fields maybe associated with different types of events that possibly havedifferent data formats and different extraction rules. By enabling acommon field name to be used to identify equivalent fields fromdifferent types of events generated by disparate data sources, thesystem facilitates use of a “common information model” (CIM) across thedisparate data sources (further discussed with respect to FIG. 7).

3.0. Operating Environment

FIG. 3 illustrates a networked computer system 26 in which an embodimentmay be implemented. Those skilled in the art would understand that FIG.3 represents one example of a networked computer system and otherembodiments may use different arrangements.

The networked computer system 26 comprises one or more computingdevices. These one or more computing devices comprise any combination ofhardware and software configured to implement the various logicalcomponents described herein. For example, the one or more computingdevices may include one or more memories that store instructions forimplementing the various components described herein, one or morehardware processors configured to execute the instructions stored in theone or more memories, and various data repositories in the one or morememories for storing data structures utilized and manipulated by thevarious components.

In an embodiment, one or more client devices 28 are coupled to one ormore host devices 30 and a data intake and query system 32 via one ormore networks 34. Networks 34 broadly represent one or more LANs, WANs,cellular networks (e.g., LTE, HSPA, 3G, and other cellulartechnologies), and/or networks using any of wired, wireless, terrestrialmicrowave, or satellite links, and may include the public Internet.

3.1. Host Devices

In the illustrated embodiment, a system 26 includes one or more hostdevices 30. Host devices 30 may broadly include any number of computers,virtual machine instances, and/or data centers that are configured tohost or execute one or more instances of host applications 36. Ingeneral, a host device 30 may be involved, directly or indirectly, inprocessing requests received from client devices 28. Each host device 30may comprise, for example, one or more of a network device, a webserver, an application server, a database server, etc. A collection ofhost devices 30 may be configured to implement a network-based service.For example, a provider of a network-based service may configure one ormore host devices 30 and host applications 36 (e.g., one or more webservers, application servers, database servers, etc.) to collectivelyimplement the network-based application.

In general, client devices 28 communicate with one or more hostapplications 36 to exchange information. The communication between aclient device 28 and a host application 36 may, for example, be based onthe Hypertext Transfer Protocol (HTTP) or any other network protocol.Content delivered from the host application 36 to a client device 28 mayinclude, for example, HTML documents, media content, etc. Thecommunication between a client device 28 and host application 36 mayinclude sending various requests and receiving data packets. Forexample, in general, a client device 28 or application running on aclient device may initiate communication with a host application 36 bymaking a request for a specific resource (e.g., based on an HTTPrequest), and the application server may respond with the requestedcontent stored in one or more response packets.

In the illustrated embodiment, one or more of host applications 36 maygenerate various types of performance data during operation, includingevent logs, network data, sensor data, and other types ofmachine-generated data. For example, a host application 36 comprising aweb server may generate one or more web server logs in which details ofinteractions between the web server and any number of client devices 28is recorded. As another example, a host device 30 comprising a routermay generate one or more router logs that record information related tonetwork traffic managed by the router. As yet another example, a hostapplication 36 comprising a database server may generate one or morelogs that record information related to requests sent from other hostapplications 36 (e.g., web servers or application servers) for datamanaged by the database server.

3.2. Client Devices

Client devices 28 of FIG. 3 represent any computing device capable ofinteracting with one or more host devices 30 via a network 34. Examplesof client devices 28 may include, without limitation, smart phones,tablet computers, handheld computers, wearable devices, laptopcomputers, desktop computers, servers, portable media players, gamingdevices, and so forth. In general, a client device 28 can provide accessto different content, for instance, content provided by one or more hostdevices 30, etc. Each client device 28 may comprise one or more clientapplications 38, described in more detail in a separate sectionhereinafter.

3.3. Client Device Applications

In an embodiment, each client device 28 may host or execute one or moreclient applications 38 that are capable of interacting with one or morehost devices 30 via one or more networks 34. For instance, a clientapplication 28 may be or comprise a web browser that a user may use tonavigate to one or more websites or other resources provided by one ormore host devices 30. As another example, a client application 38 maycomprise a mobile application or “app.” For example, an operator of anetwork-based service hosted by one or more host devices 30 may makeavailable one or more mobile apps that enable users of client devices 28to access various resources of the network-based service. As yet anotherexample, client applications 38 may include background processes thatperform various operations without direct interaction from a user. Aclient application 38 may include a “plug-in” or “extension” to anotherapplication, such as a web browser plug-in or extension.

In an embodiment, a client application 38 may include a monitoringcomponent 40. At a high level, the monitoring component 40 comprises asoftware component or other logic that facilitates generatingperformance data related to a client device's operating state, includingmonitoring network traffic sent and received from the client device andcollecting other device and/or application-specific information.Monitoring component 40 may be an integrated component of a clientapplication 38, a plug-in, an extension, or any other type of add-oncomponent. Monitoring component 40 may also be a stand-alone process.

In one embodiment, a monitoring component 40 may be created when aclient application 38 is developed, for example, by an applicationdeveloper using a software development kit (SDK). The SDK may includecustom monitoring code that can be incorporated into the codeimplementing a client application 38. When the code is converted to anexecutable application, the custom code implementing the monitoringfunctionality can become part of the application itself.

In some cases, an SDK or other code for implementing the monitoringfunctionality may be offered by a provider of a data intake and querysystem, such as a system 32. In such cases, the provider of the system26 can implement the custom code so that performance data generated bythe monitoring functionality is sent to the system 32 to facilitateanalysis of the performance data by a developer of the clientapplication or other users.

In an embodiment, the custom monitoring code may be incorporated intothe code of a client application 38 in a number of different ways, suchas the insertion of one or more lines in the client application codethat call or otherwise invoke the monitoring component 40. As such, adeveloper of a client application 38 can add one or more lines of codeinto the client application 38 to trigger the monitoring component 40 atdesired points during execution of the application. Code that triggersthe monitoring component may be referred to as a monitor trigger. Forinstance, a monitor trigger may be included at or near the beginning ofthe executable code of the client application 38 such that themonitoring component 40 is initiated or triggered as the application islaunched, or included at other points in the code that correspond tovarious actions of the client application, such as sending a networkrequest or displaying a particular interface.

In an embodiment, the monitoring component 40 may monitor one or moreaspects of network traffic sent and/or received by a client application38. For example, the monitoring component 40 may be configured tomonitor data packets transmitted to and/or from one or more hostapplications 36. Incoming and/or outgoing data packets can be read orexamined to identify network data contained within the packets, forexample, and other aspects of data packets can be analyzed to determinea number of network performance statistics. Monitoring network trafficmay enable information to be gathered particular to the networkperformance associated with a client application 38 or set ofapplications.

In an embodiment, network performance data refers to any type of datathat indicates information about the network and/or network performance.Network performance data may include, for instance, a URL requested, aconnection type (e.g., HTTP, HTTPS, etc.), a connection start time, aconnection end time, an HTTP status code, request length, responselength, request headers, response headers, connection status (e.g.,completion, response time(s), failure, etc.), and the like. Uponobtaining network performance data indicating performance of thenetwork, the network performance data can be transmitted to a dataintake and query system 32 for analysis.

Upon developing a client application 38 that incorporates a monitoringcomponent 40, the client application 38 can be distributed to clientdevices 28. Applications generally can be distributed to client devices28 in any manner, or they can be pre-loaded. In some cases, theapplication may be distributed to a client device 28 via an applicationmarketplace or other application distribution system. For instance, anapplication marketplace or other application distribution system mightdistribute the application to a client device based on a request fromthe client device to download the application.

Examples of functionality that enables monitoring performance of aclient device are described in U.S. patent application Ser. No.14/524,748, entitled “UTILIZING PACKET HEADERS TO MONITOR NETWORKTRAFFIC IN ASSOCIATION WITH A CLIENT DEVICE,” filed on 27 Oct. 2014, andwhich is hereby incorporated by reference in its entirety for allpurposes.

In an embodiment, the monitoring component 40 may also monitor andcollect performance data related to one or more aspects of theoperational state of a client application 38 and/or client device 28.For example, a monitoring component 40 may be configured to collectdevice performance information by monitoring one or more client deviceoperations, or by making calls to an operating system and/or one or moreother applications executing on a client device 28 for performanceinformation. Device performance information may include, for instance, acurrent wireless signal strength of the device, a current connectiontype and network carrier, current memory performance information, ageographic location of the device, a device orientation, and any otherinformation related to the operational state of the client device.

In an embodiment, the monitoring component 40 may also monitor andcollect other device profile information including, for example, a typeof client device, a manufacturer and model of the device, versions ofvarious software applications installed on the device, and so forth.

In general, a monitoring component 40 may be configured to generateperformance data in response to a monitor trigger in the code of aclient application 38 or other triggering application event, asdescribed above, and to store the performance data in one or more datarecords. Each data record, for example, may include a collection offield-value pairs, each field-value pair storing a particular item ofperformance data in association with a field for the item. For example,a data record generated by a monitoring component 40 may include a“networkLatency” field (not shown in the Figure) in which a value isstored. This field indicates a network latency measurement associatedwith one or more network requests. The data record may include a “state”field to store a value indicating a state of a network connection, andso forth for any number of aspects of collected performance data.

3.4. Data Server System

FIG. 4 depicts a block diagram of an exemplary data intake and querysystem 32, similar to the SPLUNK® ENTERPRISE system. System 32 includesone or more forwarders 42 that receive data from a variety of input datasources 44, and one or more indexers 46 that process and store the datain one or more data stores 48 (internal data stores). These forwardersand indexers can comprise separate computer systems, or mayalternatively comprise separate processes executing on one or morecomputer systems.

Each data source 44 broadly represents a distinct source of data thatcan be consumed by a system 32. Examples of a data source 44 include,without limitation, data files, directories of files, data sent over anetwork, event logs, registries, etc.

During operation, the forwarders 42 identify which indexers 46 receivedata collected from a data source 44 and forward the data to theappropriate indexers 46. Forwarders 42 can also perform operations onthe data before forwarding, including removing extraneous data,detecting timestamps in the data, parsing data, indexing data, routingdata based on criteria relating to the data being routed, and/orperforming other data transformations.

In an embodiment, a forwarder 42 may comprise a service accessible toclient devices 28 and host devices 30 via a network 34. For example, onetype of forwarder 42 may be capable of consuming vast amounts ofreal-time data from a potentially large number of client devices 28and/or host devices 30. The forwarder 42 may, for example, comprise acomputing device which implements multiple data pipelines or “queues” tohandle forwarding of network data to indexers 46. A forwarder 42 mayalso perform many of the functions that are performed by an indexer. Forexample, a forwarder 42 may perform keyword extractions on raw data orparse raw data to create events. A forwarder 42 may generate time stampsfor events.

Additionally or alternatively, a forwarder 42 may perform routing ofevents to indexers. Data store 48 may contain events derived frommachine data from a variety of sources all pertaining to the samecomponent in an IT environment, and this data may be produced by themachine in question or by other components in the IT environment. Thedata intake and query system 32 also include a search head 50, whichwill be described in greater details further below.

3.5. Data Ingestion

FIG. 5 depicts a flow chart illustrating an example data flow performedby data intake and query system 32, in accordance with the disclosedembodiments. The data flow illustrated in FIG. 5 is provided forillustrative purposes only; those skilled in the art would understandthat one or more of the steps of the processes illustrated in FIG. 5 maybe removed or the ordering of the steps may be changed. Furthermore, forthe purposes of illustrating a clear example, one or more particularsystem components are described in the context of performing variousoperations during each of the data flow stages. For example, a forwarderis described as receiving and processing data during an input phase; anindexer is described as parsing and indexing data during parsing andindexing phases; and a search head is described as performing a searchquery during a search phase. However, other system arrangements anddistributions of the processing steps across system components may beused.

3.5.1. Input

In step 502, a forwarder receives data from an input source, such as adata source 44 shown in FIG. 4. A forwarder initially may receive thedata as a raw data stream generated by the input source. For example, aforwarder may receive a data stream from a log file generated by anapplication server, from a stream of network data from a network device,or from any other source of data. In one embodiment, a forwarderreceives the raw data and may segment the data stream into “blocks”, or“buckets,” possibly of a uniform data size, to facilitate subsequentprocessing steps.

In step 504, a forwarder or other system component annotates each blockgenerated from the raw data with one or more metadata fields. Thesemetadata fields may, for example, provide information related to thedata block as a whole and may apply to each event that is subsequentlyderived from the data in the data block. For example, the metadatafields may include separate fields specifying each of a host, a source,and a source type related to the data block. A host field may contain avalue identifying a host name or IP address of a device that generatedthe data. A source field may contain a value identifying a source of thedata, such as a pathname of a file or a protocol and port related toreceived network data. A source type field may contain a valuespecifying a particular source type label for the data. Additionalmetadata fields may also be included during the input phase, such as acharacter encoding of the data, if known, and possibly other values thatprovide information relevant to later processing steps. In anembodiment, a forwarder forwards the annotated data blocks to anothersystem component (typically an indexer) for further processing.

The SPLUNK® ENTERPRISE system allows forwarding of data from one SPLUNK®ENTERPRISE instance to another, or even to a third-party system. SPLUNK®ENTERPRISE system can employ different types of forwarders in aconfiguration.

In an embodiment, a forwarder may contain the essential componentsneeded to forward data. It can gather data from a variety of inputs andforward the data to a SPLUNK® ENTERPRISE server for indexing andsearching. It also can tag metadata (e.g., source, source type, host,etc.).

Additionally or optionally, in an embodiment, a forwarder has thecapabilities of the aforementioned forwarder as well as additionalcapabilities. The forwarder can parse data before forwarding the data(e.g., associate a time stamp with a portion of data and create anevent, etc.) and can route data based on criteria such as source or typeof event. It can also index data locally while forwarding the data toanother indexer.

3.5.2. Parsing

In step 506, an indexer receives data blocks from a forwarder and parsesthe data to organize the data into events. In an embodiment, to organizethe data into events, an indexer may determine a source type associatedwith each data block (e.g., by extracting a source type label from themetadata fields associated with the data block, etc.) and refer to asource type configuration corresponding to the identified source type.The source type definition may include one or more properties thatindicate to the indexer to automatically determine the boundaries ofevents within the data. In general, these properties may include regularexpression-based rules or delimiter rules where, for example, eventboundaries may be indicated by predefined characters or characterstrings. These predefined characters may include punctuation marks orother special characters including, for example, carriage returns, tabs,spaces, line breaks, etc. If a source type for the data is unknown tothe indexer, an indexer may infer a source type for the data byexamining the structure of the data. Then, it can apply an inferredsource type definition to the data to create the events.

In step 508, the indexer determines a timestamp for each event. Similarto the process for creating events, an indexer may again refer to asource type definition associated with the data to locate one or moreproperties that indicate instructions for determining a timestamp foreach event. The properties may, for example, instruct an indexer toextract a time value from a portion of data in the event, to interpolatetime values based on timestamps associated with temporally proximateevents, to create a timestamp based on a time the event data wasreceived or generated, to use the timestamp of a previous event, or useany other rules for determining timestamps.

In step 510, the indexer associates with each event one or more metadatafields including a field containing the timestamp (in some embodiments,a timestamp may be included in the metadata fields) determined for theevent. These metadata fields may include a number of “default fields”that are associated with all events, and may also include one morecustom field as defined by a user. Similar to the metadata fieldsassociated with the data blocks at block 504, the default metadatafields associated with each event may include a host, source, and sourcetype field including or in addition to a field storing the timestamp.

In step 512, an indexer may optionally apply one or more transformationsto data included in the events created at block 506. For example, suchtransformations can include removing a portion of an event (e.g., aportion used to define event boundaries, extraneous characters from theevent, other extraneous text, etc.), masking a portion of an event(e.g., masking a credit card number), removing redundant portions of anevent, etc. The transformations applied to event data may, for example,be specified in one or more configuration files and referenced by one ormore source type definitions.

3.5.3. Indexing

In steps 514 and 516, an indexer can optionally generate a keyword indexto facilitate fast keyword searching for event data. To build a keywordindex, in step 514, the indexer identifies a set of keywords in eachevent. In step 516, the indexer includes the identified keywords in anindex, which associates each stored keyword with reference pointers toevents containing that keyword (or to locations within events where thatkeyword is located, other location identifiers, etc.). When an indexersubsequently receives a keyword-based query, the indexer can access thekeyword index to quickly identify events containing the keyword.

In some embodiments, the keyword index may include entries forname-value pairs found in events, where a name-value pair can include apair of keywords connected by a symbol, such as an equals sign or colon.This way, events containing these name-value pairs can be quicklylocated. In some embodiments, fields can automatically be generated forsome or all of the name-value pairs at the time of indexing. Forexample, if the string “dest=10.0.1.2” is found in an event, a fieldnamed “dest” may be created for the event, and assigned a value of“10.0.1.2”.

In step 518, the indexer stores the events with an associated timestampin a data store 48. Timestamps enable a user to search for events basedon a time range. In one embodiment, the stored events are organized into“buckets,” where each bucket stores events associated with a specifictime range based on the timestamps associated with each event. This maynot only improve time-based searching, but also allows for events withrecent timestamps, which may have a higher likelihood of being accessed,to be stored in a faster memory to facilitate faster retrieval. Forexample, buckets containing the most recent events can be stored inflash memory rather than on a hard disk.

Each indexer 46 may be responsible for storing and searching a subset ofthe events contained in a corresponding data store 48. By distributingevents among the indexers and data stores, the indexers can analyzeevents for a query in parallel. For example, using map-reducetechniques, each indexer returns partial search results for a subset ofevents to a search head that combines the results to produce an answerfor the query. By storing events in buckets for specific time ranges, anindexer may further optimize data retrieval process by searching bucketscorresponding to time ranges that are relevant to a query.

Moreover, events and buckets can also be replicated across differentindexers and data stores to facilitate high availability and disasterrecovery as described in U.S. patent application Ser. No. 14/266,812,entitled “SITE-BASED SEARCH AFFINITY,” filed on 30 Apr. 2014, and inU.S. patent application Ser. No. 14/266,817, entitled “MULTI-SITECLUSTERING,” also filed on 30 Apr. 2014, each of which is herebyincorporated by reference in its entirety for all purposes.

3.6. Query Processing

FIG. 6 is a flow diagram that illustrates an exemplary process that asearch head and one or more indexers may perform during a search query.In step 602, a search head receives a search query from a client. Instep 604, the search head analyzes the search query to determine whatportion(s) of the query can be delegated to indexers and what portionsof the query can be executed locally by the search head. In step 606,the search head distributes the determined portions of the query to theappropriate indexers. In an embodiment, a search head cluster may takethe place of an independent search head where each search head in thesearch head cluster coordinates with peer search heads in the searchhead cluster to schedule jobs, replicate search results, updateconfigurations, fulfill search requests, etc. In an embodiment, thesearch head (or each search head) communicates with a lead search head(also known as a cluster leader, not shown in Fig.) that provides thesearch head with a list of indexers to which the search head candistribute the determined portions of the query. The leader maintains alist of active indexers and can also designate which indexers may haveresponsibility for responding to queries over certain sets of events. Asearch head may communicate with the leader before the search headdistributes queries to indexers to discover the addresses of activeindexers.

In step 608, the indexers to which the query was distributed, searchdata stores associated with them for events that are responsive to thequery. To determine which events are responsive to the query, theindexer searches for events that match the criteria specified in thequery. These criteria can include matching keywords or specific valuesfor certain fields. The searching operations at step 608 may use thelate-binding schema to extract values for specified fields from eventsat the time the query is processed. In an embodiment, one or more rulesfor extracting field values may be specified as part of a source typedefinition. The indexers may then either send the relevant events backto the search head, or use the events to determine a partial result, andsend the partial result back to the search head.

In step 610, the search head combines the partial results and/or eventsreceived from the indexers to produce a final result for the query. Thisfinal result may comprise different types of data depending on what thequery requested. For example, the results can include a listing ofmatching events returned by the query, or some type of visualization ofthe data from the returned events. In another example, the final resultcan include one or more calculated values derived from the matchingevents.

The results generated by the system 32 can be returned to a client usingdifferent techniques. For example, one technique streams results orrelevant events back to a client in real-time as they are identified.Another technique waits to report the results to the client until acomplete set of results (which may include a set of relevant events or aresult based on relevant events) is ready to return to the client. Yetanother technique streams interim results or relevant events back to theclient in real-time until a complete set of results is ready, and thenreturns the complete set of results to the client. In another technique,certain results are stored as “search jobs” and the client may retrievethe results by referring to the search jobs.

The search head can also perform various operations to make the searchmore efficient. For example, before the search head begins execution ofa query, the search head can determine a time range for the query and aset of common keywords that all matching events include. The search headmay then use these parameters to query the indexers to obtain a supersetof the eventual results. Then, during a filtering stage, the search headcan perform field-extraction operations on the superset to produce areduced set of search results. This speeds up queries that are performedon a periodic basis.

3.7. Field Extraction

Referring back to FIG. 4, the search head 50 allows users to search andvisualize event data extracted from raw machine data received fromhomogenous data sources. It also allows users to search and visualizeevent data extracted from raw machine data received from heterogeneousdata sources. The search head 50 includes various mechanisms, which mayadditionally reside in an indexer 46, for processing a query. SplunkProcessing Language (SPL), used in conjunction with the SPLUNK®ENTERPRISE system, can be utilized to make a query. SPL is a pipelinedsearch language in which a set of inputs is operated on by a firstcommand in a command line, and then a subsequent command following thepipe symbol “|” operates on the results produced by the first command,and so on for additional commands. Other query languages, such as theStructured Query Language (“SQL”), can be used to create a query.

In response to receiving the search query, search head 50 usesextraction rules to extract values for the fields associated with afield or fields in the event data being searched. The search head 50obtains extraction rules that specify how to extract a value for certainfields from an event. Extraction rules can comprise regex rules thatspecify how to extract values for the relevant fields. In addition tospecifying how to extract field values, the extraction rules may alsoinclude instructions for deriving a field value by performing a functionon a character string or value retrieved by the extraction rule. Forexample, a transformation rule may truncate a character string, orconvert the character string into a different data format. In somecases, the query itself can specify one or more extraction rules.

The search head 50 can apply the extraction rules to event data that itreceives from indexers 46. Indexers 46 may apply the extraction rules toevents in an associated data store 48. Extraction rules can be appliedto all the events in a data store, or to a subset of the events thathave been filtered based on some criteria (e.g., event time stampvalues, etc.). Extraction rules can be used to extract one or morevalues for a field from events by parsing the event data and examiningthe event data for one or more patterns of characters, numbers,delimiters, etc., that indicate where the field begins and, optionally,ends.

FIG. 7 illustrates an example of raw machine data received fromdisparate data sources. In this example, a user submits an order formerchandise using a vendor's shopping application program 52 running onthe user's system. In this example, the order was not delivered to thevendor's server due to a resource exception at the destination serverthat is detected by the middleware code 54. The user then sends amessage to the customer support 56 to complain about the order failingto complete. The three systems 52, 54, and 56 are disparate systems thatdo not have a common logging format. The order application 52 sends logdata 58 to the SPLUNK® ENTERPRISE system in one format, the middlewarecode 54 sends error log data 60 in a second format, and the supportserver 56 sends log data 62 in a third format.

Using the log data received at one or more indexers 46 from the threesystems, the vendor can uniquely obtain an insight into user activity,user experience, and system behavior. The search head 50 allows thevendor's administrator to search the log data from the three systemsthat one or more indexers 46 are responsible for searching, therebyobtaining correlated information, such as the order number andcorresponding customer ID number of the person placing the order. Thesystem also allows the administrator to see a visualization of relatedevents via a user interface. The administrator can query the search head50 for customer ID field value matches across the log data from thethree systems that are stored at the one or more indexers 46. Thecustomer ID field value exists in the data gathered from the threesystems, but the customer ID field value may be located in differentareas of the data given differences in the architecture of thesystems—there is a semantic relationship between the customer ID fieldvalues generated by the three systems. The search head 50 requests eventdata from the one or more indexers 46 to gather relevant event data fromthe three systems. It then applies extraction rules to the event data inorder to extract field values that it can correlate. The search head mayapply a different extraction rule to each set of events from each systemwhen the event data format differs among systems. In this example, theuser interface can display to the administrator the event datacorresponding to the common customer ID field values 64, 66, and 68,thereby providing the administrator with insight into a customer'sexperience.

Note that query results can be returned to a client, a search head, orany other system component for further processing. In general, queryresults may include a set of one or more events, a set of one or morevalues obtained from the events, a subset of the values, statisticscalculated based on the values, a report containing the values, or avisualization, such as a graph or chart, generated from the values.

3.8. Example Search Screen

FIG. 8A illustrates an example search screen 70 in accordance with thedisclosed embodiments. Search screen 70 includes a search bar 72 thataccepts user input in the form of a search string. It also includes atime range picker 74 that enables the user to specify a time range forthe search. For “historical searches” the user can select a specifictime range, or alternatively a relative time range, such as “today,”“yesterday” or “last week.” For “real-time searches,” the user canselect the size of a preceding time window to search for real-timeevents. Search screen 70 also initially displays a “data summary” dialogas is illustrated in FIG. 8B that enables the user to select differentsources for the event data, such as by selecting specific hosts and logfiles.

After the search is executed, the search screen 70 in FIG. 8A candisplay the results through search results tabs 76, wherein searchresults tabs 76 includes: an “events tab” that displays variousinformation about events returned by the search; a “statistics tab” thatdisplays statistics about the search results; and a “visualization tab”that displays various visualizations of the search results. The eventstab illustrated in FIG. 8A displays a timeline graph 78 that graphicallyillustrates the number of events that occurred in one-hour intervalsover the selected time range. It also displays an events list 80 thatenables a user to view the raw data in each of the returned events. Itadditionally displays a fields sidebar 82 that includes statistics aboutoccurrences of specific fields in the returned events, including“selected fields” that are pre-selected by the user, and “interestingfields” that are automatically selected by the system based onpre-specified criteria.

3.9. Data Models

A data model is a hierarchically structured search-time mapping ofsemantic knowledge about one or more datasets. It encodes the domainknowledge necessary to build a variety of specialized searches of thosedatasets. Those searches, in turn, can be used to generate reports.

A data model is composed of one or more “objects” (or “data modelobjects”) that define or otherwise correspond to a specific set of data.

Objects in data models can be arranged hierarchically in parent/childrelationships. Each child object represents a subset of the datasetcovered by its parent object. The top-level objects in data models arecollectively referred to as “root objects.”

Child objects have inheritance. Data model objects are defined bycharacteristics that mostly break down into constraints and attributes.Child objects inherit constraints and attributes from their parentobjects and have additional constraints and attributes of their own.Child objects provide a way of filtering events from parent objects.Because a child object always provides an additional constraint inaddition to the constraints it has inherited from its parent object, thedataset it represents is always a subset of the dataset that its parentrepresents.

For example, a first data model object may define a broad set of datapertaining to e-mail activity generally, and another data model objectmay define specific datasets within the broad dataset, such as a subsetof the e-mail data pertaining specifically to e-mails sent. Examples ofdata models can include electronic mail, authentication, databases,intrusion detection, malware, application state, alerts, computeinventory, network sessions, network traffic, performance, audits,updates, vulnerabilities, etc. Data models and their objects can bedesigned by knowledge managers in an organization, and they can enabledownstream users to quickly focus on a specific set of data. Forexample, a user can simply select an “e-mail activity” data model objectto access a dataset relating to e-mails generally (e.g., sent orreceived), or select an “e-mails sent” data model object (or datasub-model object) to access a dataset relating to e-mails sent.

A data model object may be defined by (1) a set of search constraints,and (2) a set of fields. Thus, a data model object can be used toquickly search data to identify a set of events and to identify a set offields to be associated with the set of events. For example, an “e-mailssent” data model object may specify a search for events relating toe-mails that have been sent, and specify a set of fields that areassociated with the events. Thus, a user can retrieve and use the“e-mails sent” data model object to quickly search source data forevents relating to sent e-mails, and may be provided with a listing ofthe set of fields relevant to the events in a user interface screen.

A child of the parent data model may be defined by a search (typically anarrower search) that produces a subset of the events that would beproduced by the parent data model's search. The child's set of fieldscan include a subset of the set of fields of the parent data modeland/or additional fields. Data model objects that reference the subsetscan be arranged in a hierarchical manner, so that child subsets ofevents are proper subsets of their parents. A user iteratively applies amodel development tool (not shown in Fig.) to prepare a query thatdefines a subset of events and assigns an object name to that subset. Achild subset is created by further limiting a query that generated aparent subset. A late-binding schema of field extraction rules isassociated with each object or subset in the data model.

Data definitions in associated schemas can be taken from the commoninformation model (CIM) or can be devised for a particular schema andoptionally added to the CIM. Child objects inherit fields from parentsand can include fields not present in parents. A model developer canselect fewer extraction rules than are available for the sourcesreturned by the query that defines events belonging to a model.Selecting a limited set of extraction rules can be a tool forsimplifying and focusing the data model, while allowing a userflexibility to explore the data subset. Development of a data model isfurther explained in U.S. Pat. Nos. 8,788,525 and 8,788,526, bothentitled “DATA MODEL FOR MACHINE DATA FOR SEMANTIC SEARCH,” both issuedon 22 Jul. 2014, U.S. Pat. No. 8,983,994, entitled “GENERATION OF A DATAMODEL FOR SEARCHING MACHINE DATA,” issued on 17 Mar. 2015, U.S. patentapplication Ser. No. 14/611,232, entitled “GENERATION OF A DATA MODELAPPLIED TO QUERIES”, filed on 31 Jan. 2015, and U.S. patent applicationSer. No. 14/815,884, entitled “GENERATION OF A DATA MODEL APPLIED TOOBJECT QUERIES,” filed on 31 Jul. 2015, each of which is herebyincorporated by reference in its entirety for all purposes. See, also,Knowledge Manager Manual, Build a Data Model, Splunk Enterprise 6.1.3pp. 150-204 (Aug. 25, 2014).

A data model can also include reports. One or more report formats can beassociated with a particular data model and be made available to runagainst the data model. A user can use child objects to design reportswith object datasets that already have extraneous data pre-filtered out.In an embodiment, the data intake and query system 32 provides the userwith the ability to produce reports (e.g., a table, chart,visualization, etc.) without having to enter SPL, SQL, or other querylanguage terms into a search screen. Data models are used as the basisfor the search feature.

Data models may be selected in a report generation interface. The reportgenerator supports drag-and-drop organization of fields to be summarizedin a report. When a model is selected, the fields with availableextraction rules are made available for use in the report. The user mayrefine and/or filter search results to produce more precise reports. Theuser may select some fields for organizing the report and select otherfields for providing detail according to the report organization. Forexample, “region” and “salesperson” are fields used for organizing thereport and sales data can be summarized (subtotaled and totaled) withinthis organization. The report generator allows the user to specify oneor more fields within events and apply statistical analysis on valuesextracted from the specified one or more fields. The report generatormay aggregate search results across sets of events and generatestatistics based on aggregated search results. Building reports usingthe report generation interface is further explained in U.S. patentapplication Ser. No. 14/503,335, entitled “GENERATING REPORTS FROMUNSTRUCTURED DATA,” filed on 30 Sep. 2014, and which is herebyincorporated by reference in its entirety for all purposes, and in PivotManual, Splunk Enterprise 6.1.3 (Aug. 4, 2014). Data visualizations alsocan be generated in a variety of formats, by reference to the datamodel. Reports, data visualizations, and data model objects can be savedand associated with the data model for future use. The data model objectmay be used to perform searches of other data.

FIGS. 9A-9D, 14, and 15 illustrate a series of user interface screenswhere a user may select report generation options using data models. Thereport generation process may be driven by a predefined data modelobject, such as a data model object defined and/or saved via a reportingapplication or a data model object obtained from another source. A usercan load a saved data model object using a report editor. For example,the initial search query and fields used to drive the report editor maybe obtained from a data model object. The data model object that is usedto drive a report generation process may define a search and a set offields. Upon loading of the data model object, the report generationprocess may enable a user to use the fields (e.g., the fields defined bythe data model object) to define criteria for a report (e.g., filters,split rows/columns, aggregates, etc.) and the search may be used toidentify events (e.g., to identify events responsive to the search) usedto generate the report. That is, for example, if a data model object isselected to drive a report editor, the graphical user interface of thereport editor may enable a user to define reporting criteria for thereport using the fields associated with the selected data model object,and the events used to generate the report may be constrained to theevents that match, or otherwise satisfy, the search constraints of theselected data model object.

The selection of a data model object for use in driving a reportgeneration may be facilitated by a data model object selectioninterface. FIG. 14 illustrates an example interactive data modelselection graphical user interface 84 of a report editor that displays alisting of available data models 86. The user may select one of the datamodels 88.

FIG. 15 illustrates an example data model object selection graphicaluser interface 90 that displays available data objects 92 for theselected data object model 1202. The user may select one of thedisplayed data model objects 94 for use in driving the report generationprocess.

Once a data model object is selected by the user, a user interfacescreen 96 shown in FIG. 9A may display an interactive listing ofautomatic field identification options 98 based on the selected datamodel object. For example, a user may select one of the threeillustrated options (e.g., the “All Fields” option 100, the “SelectedFields” option 102, or the “Coverage” option (e.g., fields with at leasta specified % of coverage) 104). If the user selects the “All Fields”option 100, all of the fields identified from the events that werereturned in response to an initial search query may be selected. Thatis, for example, all of the fields of the identified data model objectfields may be selected. If the user selects the “Selected Fields” option102, only the fields from the fields of the identified data model objectfields that are selected by the user may be used. If the user selectsthe “Coverage” option 104, only the fields of the identified data modelobject fields meeting a specified coverage criteria may be selected. Apercent coverage may refer to the percentage of events returned by theinitial search query that a given field appears in.

Thus, for example, if an object dataset includes 10,000 events returnedin response to an initial search query, and the “avg_age” field appearsin 854 of those 10,000 events, then the “avg_age” field would have acoverage of 8.54% for that object dataset. If, for example, the userselects the “Coverage” option and specifies a coverage value of 2%, onlyfields having a coverage value equal to or greater than 2% may beselected. The number of fields corresponding to each selectable optionmay be displayed in association with each option.

For example, “97” displayed next to the “All Fields” option 100indicates that 97 fields will be selected if the “All Fields” option isselected. The “3” displayed next to the “Selected Fields” option 102indicates that 3 of the 97 fields will be selected if the “SelectedFields” option is selected. The “49” displayed next to the “Coverage”option 104 indicates that 49 of the 97 fields (e.g., the 49 fieldshaving a coverage of 2% or greater) will be selected if the “Coverage”option is selected. The number of fields corresponding to the “Coverage”option may be dynamically updated based on the specified percent ofcoverage.

FIG. 9B illustrates an example graphical user interface screen (alsocalled the pivot interface) 106 displaying the reporting application's“Report Editor” page. The screen may display interactive elements fordefining various elements of a report. For example, the page includes a“Filters” element 108, a “Split Rows” element 110, a “Split Columns”element 112, and a “Column Values” element 114. The page may include alist of search results 118. In this example, the Split Rows element 110is expanded, revealing a listing of fields 116 that can be used todefine additional criteria (e.g., reporting criteria). The listing offields 116 may correspond to the selected fields (attributes). That is,the listing of fields 116 may list only the fields previously selected,either automatically and/or manually by a user. FIG. 9C illustrates aformatting dialogue 120 that may be displayed upon selecting a fieldfrom the listing of fields 110. The dialogue can be used to format thedisplay of the results of the selection (e.g., label the column to bedisplayed as “component”).

FIG. 9D illustrates an example graphical user interface screen 106including a table of results 122 based on the selected criteriaincluding splitting the rows by the “component” field. A column 124having an associated count for each component listed in the table may bedisplayed that indicates an aggregate count of the number of times thatthe particular field-value pair (e.g., the value in a row) occurs in theset of events responsive to the initial search query.

FIG. 16 illustrates an example graphical user interface screen 126 thatallows the user to filter search results and to perform statisticalanalysis on values extracted from specific fields in the set of events.In this example, the top ten product names ranked by price are selectedas a filter 128 that causes the display of the ten most popular productssorted by price. Each row is displayed by product name and price 130.This results in each product displayed in a column labeled “productname” along with an associated price in a column labeled “price” 136.Statistical analysis of other fields in the events associated with theten most popular products has been specified as column values 132. Acount of the number of successful purchases for each product isdisplayed in column 134. This statistics may be produced by filteringthe search results by the product name, finding all occurrences of asuccessful purchase in a field within the events and generating a totalof the number of occurrences. A sum of the total sales is displayed incolumn 136, which is a result of the multiplication of the price and thenumber of successful purchases for each product.

The reporting application allows the user to create graphicalvisualizations of the statistics generated for a report. For example,FIG. 17 illustrates an example graphical user interface 140 thatdisplays a set of components and associated statistics 142. Thereporting application allows the user to select a visualization of thestatistics in a graph (e.g., bar chart, scatter plot, area chart, linechart, pie chart, radial gauge, marker gauge, filler gauge, etc.). FIG.18 illustrates an example of a bar chart visualization 144 of an aspectof the statistical data 142. FIG. 19 illustrates a scatter plotvisualization 146 of an aspect of the statistical data 142.

3.10. Acceleration Technique

The above-described system provides significant flexibility by enablinga user to analyze massive quantities of minimally processed data “on thefly” at search time instead of storing pre-specified portions of thedata in a database at ingestion time. This flexibility enables a user tosee valuable insights, correlate data, and perform subsequent queries toexamine interesting aspects of the data that may not have been apparentat ingestion time.

However, performing extraction and analysis operations at search timecan involve a large amount of data and require a large number ofcomputational operations, which can cause delays in processing thequeries. Advantageously, SPLUNK® ENTERPRISE system employs a number ofunique acceleration techniques that have been developed to speed upanalysis operations performed at search time. These techniques include:(1) performing search operations in parallel across multiple indexers;(2) using a keyword index; (3) using a high performance analytics store;and (4) accelerating the process of generating reports. These noveltechniques are described in more detail below.

3.10.1. Aggregation Technique

To facilitate faster query processing, a query can be structured suchthat multiple indexers perform the query in parallel, while aggregationof search results from the multiple indexers is performed locally at thesearch head. For example, FIG. 10 illustrates how a search query 148received from a client at a search head 50 can split into two phases,including: (1) subtasks 150 (e.g., data retrieval or simple filtering)that may be performed in parallel by indexers 46 for execution, and (2)a search results aggregation operation 152 to be executed by the searchhead when the results are ultimately collected from the indexers.

During operation, upon receiving search query 148, a search head 50determines that a portion of the operations involved with the searchquery may be performed locally by the search head. The search headmodifies search query 148 by substituting “stats” (create aggregatestatistics over results sets received from the indexers at the searchhead) with “prestats” (create statistics by the indexer from localresults set) to produce search query 150, and then distributes searchquery 150 to distributed indexers, which are also referred to as “searchpeers.” Note that search queries may generally specify search criteriaor operations to be performed on events that meet the search criteria.Search queries may also specify field names, as well as search criteriafor the values in the fields or operations to be performed on the valuesin the fields.

Moreover, the search head may distribute the full search query to thesearch peers as illustrated in FIG. 6, or may alternatively distribute amodified version (e.g., a more restricted version) of the search queryto the search peers. In this example, the indexers are responsible forproducing the results and sending them to the search head. After theindexers return the results to the search head, the search headaggregates the received results 152 to form a single search result set.By executing the query in this manner, the system effectivelydistributes the computational operations across the indexers whileminimizing data transfers.

3.10.2. Keyword Index

As described above with reference to the flow charts in FIG. 5 and FIG.6, data intake and query system 32 can construct and maintain one ormore keyword indices to quickly identify events containing specifickeywords. This technique can greatly speed up the processing of queriesinvolving specific keywords. As mentioned above, to build a keywordindex, an indexer first identifies a set of keywords. Then, the indexerincludes the identified keywords in an index, which associates eachstored keyword with references to events containing that keyword, or tolocations within events where that keyword is located. When an indexersubsequently receives a keyword-based query, the indexer can access thekeyword index to quickly identify events containing the keyword.

3.10.3. High Performance Analytics Store

To speed up certain types of queries, some embodiments of system 32create a high performance analytics store, which is referred to as a“summarization table,” that contains entries for specific field-valuepairs. Each of these entries keeps track of instances of a specificvalue in a specific field in the event data and includes references toevents containing the specific value in the specific field. For example,an example entry in a summarization table can keep track of occurrencesof the value “94107” in a “ZIP code” field of a set of events and theentry includes references to all of the events that contain the value“94107” in the ZIP code field.

This optimization technique enables the system to quickly processqueries that seek to determine how many events have a particular valuefor a particular field. To this end, the system can examine the entry inthe summarization table to count instances of the specific value in thefield without having to go through the individual events or perform dataextractions at search time. Also, if the system needs to process allevents that have a specific field-value combination, the system can usethe references in the summarization table entry to directly access theevents to extract further information without having to search all ofthe events to find the specific field-value combination at search time.

In some embodiments, the system maintains a separate summarization tablefor each of the above-described time-specific buckets that stores eventsfor a specific time range. A bucket-specific summarization tableincludes entries for specific field-value combinations that occur inevents in the specific bucket. Alternatively, the system can maintain aseparate summarization table for each indexer. The indexer-specificsummarization table includes entries for the events in a data store thatare managed by the specific indexer. Indexer-specific summarizationtables may also be bucket-specific.

The summarization table can be populated by running a periodic querythat scans a set of events to find instances of a specific field-valuecombination, or alternatively instances of all field-value combinationsfor a specific field. A periodic query can be initiated by a user, orcan be scheduled to occur automatically at specific time intervals. Aperiodic query can also be automatically launched in response to a querythat asks for a specific field-value combination.

In some cases, when the summarization tables may not cover all of theevents that are relevant to a query, the system can use thesummarization tables to obtain partial results for the events that arecovered by summarization tables, but may also have to search throughother events that are not covered by the summarization tables to produceadditional results. These additional results can then be combined withthe partial results to produce a final set of results for the query.

The summarization table and associated techniques are described in moredetail in U.S. Pat. No. 8,682,925, entitled “DISTRIBUTED HIGHPERFORMANCE ANALYTICS STORE”, issued on 25 Mar. 2014, U.S. patentapplication Ser. No. 14/170,159, entitled “SUPPLEMENTING A HIGHPERFORMANCE ANALYTICS STORE WITH EVALUATION OF INDIVIDUAL EVENTS TORESPOND TO AN EVENT QUERY”, filed on 31 Jan. 2014, and U.S. patentapplication Ser. No. 14/815,973, entitled “STORAGE MEDIUM AND CONTROLDEVICE”, filed on 21 Feb. 2014, each of which is hereby incorporated byreference in its entirety.

3.10.4. Accelerating Report Generation

In some embodiments, a data server system such as the SPLUNK® ENTERPRISEsystem can accelerate the process of periodically generating updatedreports based on query results. To accelerate this process, asummarization engine automatically examines the query to determinewhether generation of updated reports can be accelerated by creatingintermediate summaries. If reports can be accelerated, the summarizationengine periodically generates a summary covering data obtained during alatest non-overlapping time period. For example, where the query seeksevents meeting a specified criteria, a summary for the time periodincludes only events within the time period that meet the specifiedcriteria. Similarly, if the query seeks statistics calculated from theevents, such as the number of events that match the specified criteria,then the summary for the time period includes the number of events inthe period that match the specified criteria.

In addition to the creation of the summaries, the summarization engineschedules the periodic updating of the report associated with the query.During each scheduled report update, the query engine determines whetherintermediate summaries have been generated covering portions of the timeperiod covered by the report update. If so, then the report is generatedbased on the information contained in the summaries. Also, if additionalevent data has been received and has not yet been summarized, and isrequired to generate the complete report, the query can be run on thisadditional event data. Then, the results returned by this query on theadditional event data, along with the partial results obtained from theintermediate summaries, can be combined to generate the updated report.

This process is repeated each time the report is updated. Alternatively,if the system stores events in buckets covering specific time ranges,then the summaries can be generated on a bucket-by-bucket basis. Notethat producing intermediate summaries can save the work involved inre-running the query for previous time periods, so advantageously onlythe newer event data needs to be processed while generating an updatedreport. These report acceleration techniques are described in moredetail in U.S. Pat. No. 8,589,403, entitled “COMPRESSED JOURNALING INEVENT TRACKING FILES FOR METADATA RECOVERY AND REPLICATION”, issued on19 Nov. 2013, U.S. Pat. No. 8,412,696, entitled “REAL TIME SEARCHING ANDREPORTING”, issued on 2 Apr. 2011, and U.S. Pat. Nos. 8,589,375 and8,589,432, both also entitled “REAL TIME SEARCHING AND REPORTING”, bothissued on 19 Nov. 2013, each of which is hereby incorporated byreference in its entirety.

3.11. Security Features

The SPLUNK® ENTERPRISE platform provides various schemas, dashboards andvisualizations that simplify developers' task to create applicationswith additional capabilities. One such application is the SPLUNK® APPFOR ENTERPRISE SECURITY, which performs monitoring and alertingoperations and includes analytics to facilitate identifying both knownand unknown security threats based on large volumes of data stored bythe SPLUNK® ENTERPRISE system. SPLUNK® APP FOR ENTERPRISE SECURITYprovides the security practitioner with visibility intosecurity-relevant threats found in the enterprise infrastructure bycapturing, monitoring, and reporting on data from enterprise securitydevices, systems, and applications. Through the use of SPLUNK®ENTERPRISE searching and reporting capabilities, SPLUNK® APP FORENTERPRISE SECURITY provides a top-down and bottom-up view of anorganization's security posture.

The SPLUNK® APP FOR ENTERPRISE SECURITY leverages SPLUNK® ENTERPRISEsearch-time normalization techniques, saved searches, and correlationsearches to provide visibility into security-relevant threats andactivity and generate notable events for tracking. The App enables thesecurity practitioner to investigate and explore the data to find new orunknown threats that do not follow signature-based patterns.

Conventional Security Information and Event Management (SIEM) systemsthat lack the infrastructure to effectively store and analyze largevolumes of security-related data. Traditional SIEM systems typically usefixed schemas to extract data from pre-defined security-related fieldsat data ingestion time and storing the extracted data in a relationaldatabase. This traditional data extraction process (and associatedreduction in data size) that occurs at data ingestion time inevitablyhampers future incident investigations that may need original data todetermine the root cause of a security issue, or to detect the onset ofan impending security threat.

In contrast, the SPLUNK® APP FOR ENTERPRISE SECURITY system stores largevolumes of minimally processed security-related data at ingestion timefor later retrieval and analysis at search time when a live securitythreat is being investigated. To facilitate this data retrieval process,the SPLUNK® APP FOR ENTERPRISE SECURITY provides pre-specified schemasfor extracting relevant values from the different types ofsecurity-related event data and enables a user to define such schemas.

The SPLUNK® APP FOR ENTERPRISE SECURITY can process many types ofsecurity-related information. In general, this security-relatedinformation can include any information that can be used to identifysecurity threats. For example, the security-related information caninclude network-related information, such as IP addresses, domain names,asset identifiers, network traffic volume, uniform resource locatorstrings, and source addresses. The process of detecting security threatsfor network-related information is further described in U.S. Pat. No.8,826,434, entitled “SECURITY THREAT DETECTION BASED ON INDICATIONS INBIG DATA OF ACCESS TO NEWLY REGISTERED DOMAINS”, issued on 2 Sep. 2014,U.S. patent application Ser. No. 13/956,252, entitled “INVESTIGATIVE ANDDYNAMIC DETECTION OF POTENTIAL SECURITY-THREAT INDICATORS FROM EVENTS INBIG DATA”, filed on 31 Jul. 2013, U.S. patent application Ser. No.14/445,018, entitled “GRAPHIC DISPLAY OF SECURITY THREATS BASED ONINDICATIONS OF ACCESS TO NEWLY REGISTERED DOMAINS”, filed on 28 Jul.2014, U.S. patent application Ser. No. 14/445,023, entitled “SECURITYTHREAT DETECTION OF NEWLY REGISTERED DOMAINS”, filed on 28 Jul. 2014,U.S. patent application Ser. No. 14/815,971, entitled “SECURITY THREATDETECTION USING DOMAIN NAME ACCESSES”, filed on 1 Aug. 2015, and U.S.patent application Ser. No. 14/815,972, entitled “SECURITY THREATDETECTION USING DOMAIN NAME REGISTRATIONS”, filed on 1 Aug. 2015, eachof which is hereby incorporated by reference in its entirety for allpurposes. Security-related information can also include malwareinfection data and system configuration information, as well as accesscontrol information, such as login/logout information and access failurenotifications. The security-related information can originate fromvarious sources within a data center, such as hosts, virtual machines,storage devices and sensors. The security-related information can alsooriginate from various sources in a network, such as routers, switches,email servers, proxy servers, gateways, firewalls andintrusion-detection systems.

During operation, the SPLUNK® APP FOR ENTERPRISE SECURITY facilitatesdetecting “notable events” that are likely to indicate a securitythreat. These notable events can be detected in a number of ways: (1) auser can notice a correlation in the data and can manually identify acorresponding group of one or more events as “notable;” or (2) a usercan define a “correlation search” specifying criteria for a notableevent, and every time one or more events satisfy the criteria, theapplication can indicate that the one or more events are notable. A usercan alternatively select a pre-defined correlation search provided bythe application. Note that correlation searches can be run continuouslyor at regular intervals (e.g., every hour) to search for notable events.Upon detection, notable events can be stored in a dedicated “notableevents index,” which can be subsequently accessed to generate variousvisualizations containing security-related information. Also, alerts canbe generated to notify system operators when important notable eventsare discovered.

The SPLUNK® APP FOR ENTERPRISE SECURITY provides various visualizationsto aid in discovering security threats, such as a “key indicators view”that enables a user to view security metrics, such as counts ofdifferent types of notable events. For example, FIG. 11A illustrates anexample key indicators view 154 that comprises a dashboard, which candisplay a value 156, for various security-related metrics, such asmalware infections 158. It can also display a change in a metric value160, which indicates that the number of malware infections increased by63 during the preceding interval. Key indicators view 154 additionallydisplays a histogram panel 162 that displays a histogram of notableevents organized by urgency values, and a histogram of notable eventsorganized by time intervals. This key indicators view is described infurther detail in pending U.S. patent application Ser. No. 13/956,338,entitled “KEY INDICATORS VIEW”, filed on 31 Jul. 2013, and which ishereby incorporated by reference in its entirety for all purposes.

These visualizations can also include an “incident review dashboard”that enables a user to view and act on “notable events.” These notableevents can include: (1) a single event of high importance, such as anyactivity from a known web attacker; or (2) multiple events thatcollectively warrant review, such as a large number of authenticationfailures on a host followed by a successful authentication.

For example, FIG. 11B illustrates an example incident review dashboard164 that includes a set of incident attribute fields 166 that, forexample, enables a user to specify a time range field 168 for thedisplayed events. It also includes a timeline 170 that graphicallyillustrates the number of incidents that occurred in time intervals overthe selected time range. It additionally displays an events list 172that enables a user to view a list of all of the notable events thatmatch the criteria in the incident attributes fields 166. To facilitateidentifying patterns among the notable events, each notable event can beassociated with an urgency value (e.g., low, medium, high, critical),which is indicated in the incident review dashboard. The urgency valuefor a detected event can be determined based on the severity of theevent and the priority of the system component associated with theevent.

3.12. Data Center Monitoring

As mentioned above, the SPLUNK® ENTERPRISE platform provides variousfeatures that simplify the developer's task to create variousapplications. One such application is SPLUNK® APP FOR VMWARE® thatprovides operational visibility into granular performance metrics, logs,tasks and events, and topology from hosts, virtual machines and virtualcenters. It empowers administrators with an accurate real-time pictureof the health of the environment, proactively identifying performanceand capacity bottlenecks.

Conventional data-center-monitoring systems lack the infrastructure toeffectively store and analyze large volumes of machine-generated data,such as performance information and log data obtained from the datacenter. In conventional data-center-monitoring systems,machine-generated data is typically pre-processed prior to being stored,for example, by extracting pre-specified data items and storing them ina database to facilitate subsequent retrieval and analysis at searchtime. However, the rest of the data is not saved and discarded duringpre-processing.

In contrast, the SPLUNK® APP FOR VMWARE® stores large volumes ofminimally processed machine data, such as performance information andlog data, at ingestion time for later retrieval and analysis at searchtime when a live performance issue is being investigated. In addition todata obtained from various log files, this performance-relatedinformation can include values for performance metrics obtained throughan application programming interface (API) provided as part of thevSphere Hypervisor™ system distributed by VMware, Inc. of Palo Alto,Calif. For example, these performance metrics can include: (1)CPU-related performance metrics; (2) disk-related performance metrics;(3) memory-related performance metrics; (4) network-related performancemetrics; (5) energy-usage statistics; (6) data-traffic-relatedperformance metrics; (7) overall system availability performancemetrics; (8) cluster-related performance metrics; and (9) virtualmachine performance statistics. Such performance metrics are describedin U.S. patent application Ser. No. 14/167,316, entitled “CORRELATIONFOR USER-SELECTED TIME RANGES OF VALUES FOR PERFORMANCE METRICS OFCOMPONENTS IN AN INFORMATION-TECHNOLOGY ENVIRONMENT WITH LOG DATA FROMTHAT INFORMATION-TECHNOLOGY ENVIRONMENT”, filed on 29 Jan. 2014, andwhich is hereby incorporated by reference in its entirety for allpurposes.

To facilitate retrieving information of interest from performance dataand log files, the SPLUNK® APP FOR VMWARE® provides pre-specifiedschemas for extracting relevant values from different types ofperformance-related event data, and also enables a user to define suchschemas.

The SPLUNK® APP FOR VMWARE® additionally provides various visualizationsto facilitate detecting and diagnosing the root cause of performanceproblems. For example, one such visualization is a “proactive monitoringtree” that enables a user to easily view and understand relationshipsamong various factors that affect the performance of a hierarchicallystructured computing system. This proactive monitoring tree enables auser to easily navigate the hierarchy by selectively expanding nodesrepresenting various entities (e.g., virtual centers or computingclusters) to view performance information for lower-level nodesassociated with lower-level entities (e.g., virtual machines or hostsystems).

Example node-expansion operations are illustrated in FIG. 11C, whereinnodes 1133 and 1134 are selectively expanded. Note that nodes 1131-1139can be displayed using different patterns or colors to representdifferent performance states, such as a critical state, a warning state,a normal state or an unknown/offline state. The ease of navigationprovided by selective expansion in combination with the associatedperformance-state information enables a user to quickly diagnose theroot cause of a performance problem. The proactive monitoring tree isdescribed in further detail in U.S. patent application Ser. No.14/253,490, entitled “PROACTIVE MONITORING TREE WITH SEVERITY STATESORTING”, filed on 15 Apr. 2014, and U.S. patent application Ser. No.14/812,948, also entitled “PROACTIVE MONITORING TREE WITH SEVERITY STATESORTING”, filed on 29 Jul. 2015, each of which is hereby incorporated byreference in its entirety for all purposes.

The SPLUNK® APP FOR VMWARE® also provides a user interface that enablesa user to select a specific time range and then view heterogeneous datacomprising events, log data, and associated performance metrics for theselected time range. For example, the screen illustrated in FIG. 11Ddisplays a listing of recent “tasks and events” and a listing of recent“log entries” for a selected time range above a performance-metric graphfor “average CPU core utilization” for the selected time range. Notethat a user is able to operate pull-down menus 174 to selectivelydisplay different performance metric graphs for the selected time range.This enables the user to correlate trends in the performance-metricgraph with corresponding event and log data to quickly determine theroot cause of a performance problem. This user interface is described inmore detail in U.S. patent application Ser. No. 14/167,316, entitled“CORRELATION FOR USER-SELECTED TIME RANGES OF VALUES FOR PERFORMANCEMETRICS OF COMPONENTS IN AN INFORMATION-TECHNOLOGY ENVIRONMENT WITH LOGDATA FROM THAT INFORMATION-TECHNOLOGY ENVIRONMENT,” filed on 29 Jan.2014, and which is hereby incorporated by reference in its entirety forall purposes.

3.13. Cloud-Based System Overview

The example data intake and query system 32 described in reference toFIG. 5 comprises several system components, including one or moreforwarders, indexers, and search heads. In some environments, a user ofa data intake and query system 32 may install and configure, oncomputing devices owned and operated by the user, one or more softwareapplications that implement some or all of these system components. Forexample, a user may install a software application on server computersowned by the user and configure each server to operate as one or more ofa forwarder, an indexer, a search head, etc. This arrangement generallymay be referred to as an “on-premises” solution. That is, the system 32is installed and operates on computing devices directly controlled bythe user of the system. Some users may prefer an on-premises solutionbecause it may provide a greater level of control over the configurationof certain aspects of the system (e.g., security, privacy, standards,controls, etc.). However, other users may instead prefer an arrangementin which the user is not directly responsible for providing and managingthe computing devices upon which various components of system 32operate.

In one embodiment, to provide an alternative to an entirely on-premisesenvironment for system 32, one or more of the components of a dataintake and query system instead may be provided as a cloud-basedservice. In this context, a cloud-based service refers to a servicehosted by one more computing resources that are accessible to end usersover a network, for example, by using a web browser or other applicationon a client device to interface with the remote computing resources. Forexample, a service provider may provide a cloud-based data intake andquery system by managing computing resources configured to implementvarious aspects of the system (e.g., forwarders, indexers, search heads,etc.) and by providing access to the system to end users via a network.Typically, a user may pay a subscription or other fee to use such aservice. Each subscribing user of the cloud-based service may beprovided with an account that enables the user to configure a customizedcloud-based system based on the user's preferences.

FIG. 12 illustrates a block diagram of an example cloud-based dataintake and query system. Similar to the system of FIG. 4, the networkedcomputer system 176 includes input data sources 178 and forwarders 180.These input data sources and forwarders may be in a subscriber's privatecomputing environment. Alternatively, they might be directly managed bythe service provider as part of the cloud service. In the example system176, one or more forwarders 180 and client devices 182 are coupled to acloud-based data intake and query system 184 via one or more networks185. Network 185 broadly represents one or more LANs, WANs, cellularnetworks, intranetworks, internetworks, etc., using any of wired,wireless, terrestrial microwave, satellite links, etc., and may includethe public Internet, and is used by client devices 182 and forwarders180 to access the system 184. Similar to the system of 32, each of theforwarders 180 may be configured to receive data from an input sourceand to forward the data to other components of the system 184 forfurther processing.

In an embodiment, a cloud-based data intake and query system 184 maycomprise a plurality of system instances 186. In general, each systeminstance 186 may include one or more computing resources managed by aprovider of the cloud-based system 184 made available to a particularsubscriber. The computing resources comprising a system instance 186may, for example, include one or more servers or other devicesconfigured to implement one or more forwarders, indexers, search heads,and other components of a data intake and query system, similar tosystem 32. As indicated above, a subscriber may use a web browser orother application of a client device 182 to access a web portal or otherinterface that enables the subscriber to configure an instance 186.

Providing a data intake and query system as described in reference tosystem 32 as a cloud-based service presents a number of challenges. Eachof the components of a system 32 (e.g., forwarders, indexers and searchheads) may at times refer to various configuration files stored locallyat each component. These configuration files typically may involve somelevel of user configuration to accommodate particular types of data auser desires to analyze and to account for other user preferences.However, in a cloud-based service context, users typically may not havedirect access to the underlying computing resources implementing thevarious system components (e.g., the computing resources comprising eachsystem instance 186) and may desire to make such configurationsindirectly, for example, using one or more web-based interfaces. Thus,the techniques and systems described herein for providing userinterfaces that enable a user to configure source type definitions areapplicable to both on-premises and cloud-based service contexts, or somecombination thereof (e.g., a hybrid system where both an on-premisesenvironment such as SPLUNK® ENTERPRISE and a cloud-based environmentsuch as SPLUNK CLOUD® are centrally visible).

3.14. Searching Externally Archived Data

FIG. 13 shows a block diagram of an example of a data intake and querysystem 188 that provides transparent search facilities for data systemsthat are external to the data intake and query system. Such facilitiesare available in the HUNK® system provided by Splunk Inc. of SanFrancisco, Calif. HUNK® represents an analytics platform that enablesbusiness and IT teams to rapidly explore, analyze, and visualize data inHadoop and NoSQL data stores.

The search head 190 of the data intake and query system receives searchrequests from one or more client devices 189 over network connections192. As discussed above, the data intake and query system 188 may residein an enterprise location, in the cloud, etc. FIG. 13 illustrates thatmultiple client devices 189-1, 189-2, . . . 189-3 may communicate withthe data intake and query system 188. The client devices 189 maycommunicate with the data intake and query system using a variety ofconnections. For example, one client device in FIG. 13 is illustrated ascommunicating over an Internet (Web) protocol, another client device isillustrated as communicating via a command line interface, and anotherclient device is illustrated as communicating via a system developer kit(SDK).

The search head 190 analyzes the received search request to identifyrequest parameters. If a search request received from one of the clientdevices 189 references an index maintained by the data intake and querysystem, then the search head 190 connects to one or more indexers 193 ofthe data intake and query system for the index referenced in the requestparameters. That is, if the request parameters of the search requestreference an index, then the search head accesses the data in the indexvia the indexer. The data intake and query system 188 may include one ormore indexers 193, depending on system access resources andrequirements. As described further below, the indexers 193 retrieve datafrom their respective local data stores 194 as specified in the searchrequest. The indexers and their respective data stores can comprise oneor more storage devices and typically reside on the same system, thoughthey may be connected via a local network connection.

If the request parameters of the received search request reference anexternal data collection, which is not accessible to the indexers 193 orunder the management of the data intake and query system, then thesearch head 190 can access the external data collection through anExternal Result Provider (ERP) process 196. An external data collectionmay be referred to as a “virtual index” (plural, “virtual indices”). AnERP process provides an interface through which the search head 190 mayaccess virtual indices.

Thus, a search reference to an index of the system relates to a locallystored and managed data collection. In contrast, a search reference to avirtual index relates to an externally stored and managed datacollection, which the search head may access through one or more ERPprocesses 196-1 and 196-2. FIG. 13 shows two ERP processes 196-1 and196-2 that connect to respective remote (external) virtual indices,which are indicated as a Hadoop or another system 197-1 (e.g., AmazonS3, Amazon EMR, other Hadoop Compatible File Systems (HCFS), etc.) and arelational database management system (RDBMS) 197-2. Other virtualindices may include other file organizations and protocols, such asStructured Query Language (SQL) and the like. The ellipses between theERP processes 196-1 and 196-2 indicate optional additional ERP processesof the data intake and query system 191. An ERP process may be acomputer process that is initiated or spawned by the search head 190 andis executed by the search data intake and query system 188.Alternatively or additionally, an ERP process may be a process spawnedby the search head 192 on the same or different host system as thesearch head 190 resides.

The search head 190 may spawn a single ERP process in response tomultiple virtual indices referenced in a search request, or the searchhead may spawn different ERP processes for different virtual indices.Generally, virtual indices that share common data configurations orprotocols may share ERP processes. For example, all search queryreferences to a Hadoop file system may be processed by the same ERPprocess, if the ERP process is suitably configured. Likewise, all searchquery references to an SQL database may be processed by the same ERPprocess. In addition, the search head may provide a common ERP processfor common external data source types (e.g., a common vendor may utilizea common ERP process, even if the vendor includes different data storagesystem types, such as Hadoop and SQL). Common indexing schemes also maybe handled by common ERP processes, such as flat text files or Weblogfiles.

The search head 190 determines the number of ERP processes to beinitiated via the use of configuration parameters that are included in asearch request message. Generally, there is a one-to-many relationshipbetween an external results provider “family” and ERP processes. Thereis also a one-to-many relationship between an ERP process andcorresponding virtual indices that are referred to in a search request.For example, using RDBMS, assume two independent instances of such asystem by one vendor, such as one RDBMS for production and another RDBMSused for development. In such a situation, it is likely preferable (butoptional) to use two ERP processes to maintain the independent operationas between production and development data. Both of the ERPs, however,will belong to the same family, because the two RDBMS system types arefrom the same vendor.

The ERP processes 196-1 and 196-2 receive a search request from thesearch head 190. The search head may optimize the received searchrequest for execution at the respective external virtual index.Alternatively, the ERP process may receive a search request as a resultof analysis performed by the search head or by a different systemprocess. The ERP processes 196-1 and 196-2 can communicate with thesearch head 190 via conventional input/output routines (e.g., standardin/standard out, etc.). In this way, the ERP process receives the searchrequest from a client device such that the search request may beefficiently executed at the corresponding external virtual index.

The ERP processes 196-1 and 196-2 may be implemented as a process of thedata intake and query system. Each ERP process may be provided by thedata intake and query system, or may be provided by process orapplication providers who are independent of the data intake and querysystem. Each respective ERP process may include an interface applicationinstalled at a computer of the external result provider that ensuresproper communication between the search support system and the externalresult provider. The ERP processes 196-1 and 196-2 generate appropriatesearch requests in the protocol and syntax of the respective virtualindices 197-1, 197-2, each of which corresponds to the search requestreceived by the search head 190. Upon receiving search results fromtheir corresponding virtual indices, the respective ERP process passesthe result to the search head 190, which may return or display theresults or a processed set of results based on the returned results tothe respective client device.

Client devices 189 may communicate with the data intake and query system188 through a network interface 192 (e.g., one or more LANs, WANs,cellular networks, intranetworks, and/or internetworks using any ofwired, wireless, terrestrial microwave, satellite links), and mayinclude the public Internet.

The analytics platform utilizing the External Result Provider processdescribed in more detail in U.S. Pat. No. 8,738,629, entitled “EXTERNALRESULT PROVIDED PROCESS FOR RETRIEVING DATA STORED USING A DIFFERENTCONFIGURATION OR PROTOCOL”, issued on 27 May 2014, U.S. Pat. No.8,738,587, entitled “PROCESSING A SYSTEM SEARCH REQUEST BY RETRIEVINGRESULTS FROM BOTH A NATIVE INDEX AND A VIRTUAL INDEX”, issued on 25 Jul.2013, U.S. patent application Ser. No. 14/266,832, entitled “PROCESSINGA SYSTEM SEARCH REQUEST ACROSS DISPARATE DATA COLLECTION SYSTEMS”, filedon 1 May 2014, and U.S. patent application Ser. No. 14/449,144, entitled“PROCESSING A SYSTEM SEARCH REQUEST INCLUDING EXTERNAL DATA SOURCES”,filed on 31 Jul. 2014, each of which is hereby incorporated by referencein its entirety for all purposes.

3.14.1. External Result Provider (ERP) Process Features

The ERP processes described above may include two operation modes: astreaming mode and a reporting mode. The ERP processes can operate instreaming mode only, in reporting mode only, or in both modessimultaneously. Operating in both modes simultaneously is referred to asmixed mode operation. In a mixed mode operation, the ERP at some pointcan stop providing the search head with streaming results and onlyprovide reporting results thereafter, or the search head at some pointmay start ignoring streaming results it has been using and only usereporting results thereafter.

The streaming mode returns search results in real time, with minimalprocessing, in response to the search request. The reporting modeprovides results of a search request with processing of the searchresults prior to providing them to the requesting search head, which inturn provides results to the requesting client device. ERP operationwith such multiple modes provides greater performance flexibility withregard to report time, search latency, and resource utilization.

In a mixed mode operation, both streaming mode and reporting mode areoperating simultaneously. The streaming mode results (e.g., the raw dataobtained from the external data source) are provided to the search head,which can then process the results data (e.g., break the raw data intoevents, timestamp it, filter it, etc.) and integrate the results datawith the results data from other external data sources, and/or from datastores of the search head. The search head performs such processing andcan immediately start returning interim (streaming mode) results to theuser at the requesting client device; simultaneously, the search head iswaiting for the ERP process to process the data it is retrieving fromthe external data source as a result of the concurrently executingreporting mode.

In some instances, the ERP process initially operates in a mixed mode,such that the streaming mode operates to enable the ERP quickly toreturn interim results (e.g., some of the raw or unprocessed datanecessary to respond to a search request) to the search head, enablingthe search head to process the interim results and begin providing tothe client or search requester interim results that are responsive tothe query. Meanwhile, in this mixed mode, the ERP also operatesconcurrently in reporting mode, processing portions of raw data in amanner responsive to the search query. Upon determining that it hasresults from the reporting mode available to return to the search head,the ERP may halt processing in the mixed mode at that time (or somelater time) by stopping the return of data in streaming mode to thesearch head and switching to reporting mode only. The ERP at this pointstarts sending interim results in reporting mode to the search head,which in turn may then present this processed data responsive to thesearch request to the client or search requester. Typically the searchhead switches from using results from the ERP's streaming mode ofoperation to results from the ERP's reporting mode of operation when thehigher bandwidth results from the reporting mode outstrip the amount ofdata processed by the search head in the]streaming mode of ERPoperation.

A reporting mode may have a higher bandwidth because the ERP does nothave to spend time transferring data to the search head for processingall the raw data. In addition, the ERP may optionally direct anotherprocessor to do the processing.

The streaming mode of operation does not need to be stopped to gain thehigher bandwidth benefits of a reporting mode; the search head couldsimply stop using the streaming mode results—and start using thereporting mode results—when the bandwidth of the reporting mode hascaught up with or exceeded the amount of bandwidth provided by thestreaming mode. Thus, a variety of triggers and ways to accomplish asearch head's switch from using streaming mode results to usingreporting mode results may be appreciated by one skilled in the art.

The reporting mode can involve the ERP process (or an external system)performing event breaking, time stamping, filtering of events to matchthe search query request, and calculating statistics on the results. Theuser can request particular types of data, such as if the search queryitself involves types of events, or the search request may ask forstatistics on data, such as on events that meet the search request. Ineither case, the search head understands the query language used in thereceived query request, which may be a proprietary language. Oneexemplary query language is Splunk Processing Language (SPL) developedby the assignee of the application, Splunk Inc. The search headtypically understands how to use that language to obtain data from theindexers, which store data in a format used by the SPLUNK® Enterprisesystem.

The ERP processes support the search head, as the search head is notordinarily configured to understand the format in which data is storedin external data sources such as Hadoop or SQL data systems. Rather, theERP process performs that translation from the query submitted in thesearch support system's native format (e.g., SPL if SPLUNK® ENTERPRISEis used as the search support system) to a search query request formatthat will be accepted by the corresponding external data system. Theexternal data system typically stores data in a different format fromthat of the search support system's native index format, and it utilizesa different query language (e.g., SQL or MapReduce, rather than SPL orthe like).

As noted, the ERP process can operate in the streaming mode alone. Afterthe ERP process has performed the translation of the query request andreceived raw results from the streaming mode, the search head canintegrate the returned data with any data obtained from local datasources (e.g., native to the search support system), other external datasources, and other ERP processes (if such operations were required tosatisfy the terms of the search query). An advantage of mixed modeoperation is that, in addition to streaming mode, the ERP process isalso executing concurrently in reporting mode. Thus, the ERP process(rather than the search head) is processing query results (e.g.,performing event breaking, timestamping, filtering, possibly calculatingstatistics if required to be responsive to the search query request,etc.). It should be apparent to those skilled in the art that additionaltime is needed for the ERP process to perform the processing in such aconfiguration. Therefore, the streaming mode will allow the search headto start returning interim results to the user at the client devicebefore the ERP process can complete sufficient processing to startreturning any search results. The switchover between streaming andreporting mode happens when the ERP process determines that theswitchover is appropriate, such as when the ERP process determines itcan begin returning meaningful results from its reporting mode.

The operation described above illustrates the source of operationallatency: streaming mode has low latency (immediate results) and usuallyhas relatively low bandwidth (fewer results can be returned per unit oftime). In contrast, the concurrently running reporting mode hasrelatively high latency (it has to perform a lot more processing beforereturning any results) and usually has relatively high bandwidth (moreresults can be processed per unit of time). For example, when the ERPprocess does begin returning report results, it returns more processedresults than in the streaming mode, because, for examples, statisticsonly need to be calculated to be responsive to the search request. Thatis, the ERP process doesn't have to take time to first return raw datato the search head. As noted, the ERP process could be configured tooperate in streaming mode alone and return just the raw data for thesearch head to process in a way that is responsive to the searchrequest. Alternatively, the ERP process can be configured to operate inthe reporting mode only. Also, the ERP process can be configured tooperate in streaming mode and reporting mode concurrently, as described,with the ERP process stopping the transmission of streaming results tothe search head when the concurrently running reporting mode has caughtup and started providing results. The reporting mode does not requirethe processing of all raw data that is responsive to the search queryrequest before the ERP process starts returning results; rather, thereporting mode usually performs processing of chunks of events andreturns the processing results to the search head for each chunk.

For example, an ERP process can be configured to merely return thecontents of a search result file verbatim, with little or no processingof results. That way, the search head performs all processing (such asparsing byte streams into events, filtering, etc.). The ERP process canbe configured to perform additional intelligence, such as analyzing thesearch request and handling all the computation that a native searchindexer process would otherwise perform. In this way, the configured ERPprocess provides greater flexibility in features while operatingaccording to desired preferences, such as response latency and resourcerequirements.

3.14.2. IT Service Monitoring

As previously mentioned, the SPLUNK® ENTERPRISE platform providesvarious schemas, dashboards and visualizations that make it easy fordevelopers to create applications to provide additional capabilities.One such application is SPLUNK® IT SERVICE INTELLIGENCE™, which performsmonitoring and alerting operations. It also includes analytics to helpan analyst diagnose the root cause of performance problems based onlarge volumes of data stored by the SPLUNK® ENTERPRISE system ascorrelated to the various services an IT organization provides (aservice-centric view). This differs significantly from conventional ITmonitoring systems that lack the infrastructure to effectively store andanalyze large volumes of service-related event data. Traditional servicemonitoring systems typically use fixed schemas to extract data frompre-defined fields at data ingestion time, wherein the extracted data istypically stored in a relational database. This data extraction processand associated reduction in data content that occurs at data ingestiontime inevitably hampers future investigations, when all of the originaldata may be needed to determine the root cause of or contributingfactors to a service issue.

In contrast, a SPLUNK® IT SERVICE INTELLIGENCE™ system stores largevolumes of minimally-processed service-related data at ingestion timefor later retrieval and analysis at search time, to perform regularmonitoring, or to investigate a service issue. To facilitate this dataretrieval process, SPLUNK® IT SERVICE INTELLIGENCE™ enables a user todefine an IT operations infrastructure from the perspective of theservices it provides. In this service-centric approach, a service suchas corporate e-mail may be defined in terms of the entities employed toprovide the service, such as host machines and network devices. Eachentity is defined to include information for identifying all of theevent data that pertains to the entity, whether produced by the entityitself or by another machine, and considering the many various ways theentity may be identified in raw machine data (such as by a URL, an IPaddress, or machine name). The service and entity definitions canorganize event data around a service so that all of the event datapertaining to that service can be easily identified. This capabilityprovides a foundation for the implementation of Key PerformanceIndicators.

One or more Key Performance Indicators (KPI's) are defined for a servicewithin the SPLUNK® IT SERVICE INTELLIGENCE™ application. Each KPImeasures an aspect of service performance at a point in time or over aperiod of time (aspect KPI's). Each KPI is defined by a search querythat derives a KPI value from the machine data of events associated withthe entities that provide the service. Information in the entitydefinitions may be used to identify the appropriate events at the time aKPI is defined or whenever a KPI's value is being determined. The KPIvalues derived over time may be stored to build a valuable repository ofcurrent and historical performance information for the service, and therepository, itself, may be subject to search query processing. AggregateKPIs may be defined to provide a measure of service performancecalculated from a set of service aspect KPI values; this aggregate mayeven be taken across defined timeframes and/or across multiple services.A particular service may have an aggregate KPI derived fromsubstantially all of the aspect KPI's of the service to indicate anoverall health score for the service.

SPLUNK® IT SERVICE INTELLIGENCE™ facilitates the production ofmeaningful aggregate KPI's through a system of KPI thresholds and statevalues. Different KPI definitions may produce values in differentranges, and so the same value may mean something very different from oneKPI definition to another. To address this, SPLUNK® IT SERVICEINTELLIGENCE™ implements a translation of individual KPI values to acommon domain of “state” values. For example, a KPI range of values maybe 1-100, or 50-275, while values in the state domain may be ‘critical,’‘warning,’ ‘normal,’ and ‘informational.’ Thresholds associated with aparticular KPI definition determine ranges of values for that KPI thatcorrespond to the various state values. In one case, KPI values 95-100may be set to correspond to ‘critical’ in the state domain. KPI valuesfrom disparate KPI's can be processed uniformly once they are translatedinto the common state values using the thresholds. For example, “normal80% of the time” can be applied across various KPI's. To providemeaningful aggregate KPI's, a weighting value can be assigned to eachKPI so that its influence on the calculated aggregate KPI value isincreased or decreased relative to the other KPI's.

One service in an IT environment often impacts, or is impacted by,another service. SPLUNK® IT SERVICE INTELLIGENCE™ can reflect thesedependencies. For example, a dependency relationship between a corporatee-mail service and a centralized authentication service can be reflectedby recording an association between their respective servicedefinitions. The recorded associations establish a service dependencytopology that informs the data or selection options presented in a GUI,for example. (The service dependency topology is like a “map” showinghow services are connected based on their dependencies.) The servicetopology may itself be depicted in a GUI and may be interactive to allownavigation among related services.

Entity definitions in SPLUNK® IT SERVICE INTELLIGENCE™ can includeinformational fields that can serve as metadata, implied data fields, orattributed data fields for the events identified by other aspects of theentity definition. Entity definitions in SPLUNK® IT SERVICEINTELLIGENCE™ can also be created and updated by an import of tabulardata (as represented in a CSV, another delimited file, or a search queryresult set). The import may be GUI-mediated or processed using importparameters from a GUI-based import definition process. Entitydefinitions in SPLUNK® IT SERVICE INTELLIGENCE™ can also be associatedwith a service by means of a service definition rule. Processing therule results in the matching entity definitions being associated withthe service definition. The rule can be processed at creation time, andthereafter on a scheduled or on-demand basis. This allows dynamic,rule-based updates to the service definition.

During operation, SPLUNK® IT SERVICE INTELLIGENCE™ can recognizeso-called “notable events” that may indicate a service performanceproblem or other situation of interest. These notable events can berecognized by a “correlation search” specifying trigger criteria for anotable event: every time KPI values satisfy the criteria, theapplication indicates a notable event. A severity level for the notableevent may also be specified. Furthermore, when trigger criteria aresatisfied, the correlation search may additionally or alternativelycause a service ticket to be created in an IT service management (ITSM)system, such as a system available from ServiceNow, Inc., of SantaClara, Calif.

SPLUNK® IT SERVICE INTELLIGENCE™ provides various visualizations builton its service-centric organization of event data and the KPI valuesgenerated and collected. Visualizations can be particularly useful formonitoring or investigating service performance. SPLUNK® IT SERVICEINTELLIGENCE™ provides a service monitoring interface suitable as thehome page for ongoing IT service monitoring. The interface isappropriate for settings such as desktop use or for a wall-mounteddisplay in a network operations center (NOC). The interface mayprominently display a services health section with tiles for theaggregate KPI's indicating overall health for defined services and ageneral KPI section with tiles for KPI's related to individual serviceaspects. These tiles may display KPI information in a variety of ways,such as by being colored and ordered according to factors like the KPIstate value. They also can be interactive and navigate to visualizationsof more detailed KPI information.

SPLUNK® IT SERVICE INTELLIGENCE™ provides a service-monitoring dashboardvisualization based on a user-defined template. The template can includeuser-selectable widgets of varying types and styles to display KPIinformation. The content and the appearance of widgets can responddynamically to changing KPI information. The KPI widgets can appear inconjunction with a background image, user drawing objects, or othervisual elements, that depict the IT operations environment, for example.The KPI widgets or other GUI elements can be interactive so as toprovide navigation to visualizations of more detailed KPI information.

SPLUNK® IT SERVICE INTELLIGENCE™ provides a visualization showingdetailed time-series information for multiple KPI's in parallel graphlanes. The length of each lane can correspond to a uniform time range,while the width of each lane may be automatically adjusted to fit thedisplayed KPI data. Data within each lane may be displayed in a userselectable style, such as a line, area, or bar chart. During operation auser may select a position in the time range of the graph lanes toactivate lane inspection at that point in time. Lane inspection maydisplay an indicator for the selected time across the graph lanes anddisplay the KPI value associated with that point in time for each of thegraph lanes. The visualization may also provide navigation to aninterface for defining a correlation search, using information from thevisualization to pre-populate the definition.

SPLUNK® IT SERVICE INTELLIGENCE™ provides a visualization for incidentreview showing detailed information for notable events. The incidentreview visualization may also show summary information for the notableevents over a time frame, such as an indication of the number of notableevents at each of a number of severity levels. The severity leveldisplay may be presented as a rainbow chart with the warmest colorassociated with the highest severity classification. The incident reviewvisualization may also show summary information for the notable eventsover a time frame, such as the number of notable events occurring withinsegments of the time frame. The incident review visualization maydisplay a list of notable events within the time frame ordered by anynumber of factors, such as time or severity. The selection of aparticular notable event from the list may display detailed informationabout that notable event, including an identification of the correlationsearch that generated the notable event.

SPLUNK® IT SERVICE INTELLIGENCE™ provides pre-specified schemas forextracting relevant values from the different types of service-relatedevent data. It also enables a user to define such schemas.

4.0. Data Fabric Service (DFS)

The capabilities of a data intake and query system are typically limitedto resources contained within that system. For example, the data intakeand query system has search and analytics capabilities that are limitedin scope to the indexers responsible for storing and searching a subsetof events contained in their corresponding internal data stores.

Even if a data intake and query system has access to external datastores that may include data relevant to a search query, the data intakeand query system typically has limited capabilities to process thecombination of partial search results from the indexers and externaldata sources to produce comprehensive search results. In particular, thesearch head of a data intake and query system may retrieve partialsearch results from external data systems over a network. The searchhead may also retrieve partial search results from its indexers, andcombine those partial search results with the partial search results ofthe external data sources to produce final search results for a searchquery.

For example, the search head can implement map-reduce techniques, whereeach data source returns partial search results and the search head cancombine the partial search results to produce the final search resultsof a search query. However, obtaining search results in this manner fromdistributed data systems including internal data stores and externaldata stores has limited value because the system is not scalable. Inparticular, the search head acts as a bottleneck for processing complexsearch queries on distributed data systems. The bottleneck effect at thesearch head worsens as the number of distributed data systems increases.

A solution to mitigating the bottleneck effect at a search head would beto employ a cluster of multiple search heads. Each search head couldoperate on a subset of the distributed data systems to return searchresults for that subset. For example, a search head can implementmap-reduce techniques, where each of the subset of the distributed datasystems returns partial search results to a search head that combinesthe partial search results to produce a combination of partial searchresults obtained from the subset of distributed data systems. Thecombinations of partial search results from the combination of searchheads could then be combined to produce the final search results of thesearch query. However, using multiple search heads creates unnecessarycomplexity and does not scale well as the number of distributed datasystems increases in a big data ecosystem.

Embodiments of the disclosed data fabric service (DFS) systemarchitecture overcome the aforementioned drawbacks by expanding on thecapabilities of a data intake and query system to enable application ofa search query across distributed data systems including internal datastores coupled to indexers and external data stores coupled to the dataintake and query system over a network. Moreover, the disclosedembodiments are scalable to accommodate application of a search query ona growing number of diverse data systems.

The disclosed DFS system extends the capabilities of the data intake andquery system by employing services such as a DFS search service (“searchservice”) communicatively coupled to worker nodes distributed in a bigdata ecosystem. The worker nodes are communicatively coupled to theexternal data systems that contain external data stores. The data intakeand query system can receive a search query input by a user at a clientdevice. Then, the data intake and query system can coordinate with thesearch service to execute a search scheme applied to both the indexersand the external data stores. The partial search results from theindexers and external data stores are collected by the worker nodes,which can aggregate the partial search results and transfer theaggregated partial search results back to the search service. In someembodiments, the search service can operate on the aggregate searchresults, and send finalized search results to the search head, which canrender the search results of the query on a display device.

Hence, the search head can apply a search query to the indexers andsupply the partial search results from the indexers to the worker nodes.The distributed worker nodes can act as agents of the data intake andquery system under the control of the search service to process thesearch query applied to the external data systems, and aggregate thepartial search results from both the indexers and the external datasystems. In other words, the search head of the data intake and querysystem can offload at least some query processing to the worker nodes,to both search the external data stores and harmonize the partial searchresults from both the indexers and the external data stores. This systemis scalable to accommodate any number of worker nodes communicativelycoupled to any number of external data systems.

Thus, embodiments of the DFS system can extend the capabilities of adata intake and query system by leveraging computing assets fromanywhere in a big data ecosystem to collectively execute search querieson diverse data systems regardless of whether data stores are internalof the data intake and query system and/or external data stores that arecommunicatively coupled to the data intake and query system over anetwork.

4.1. DFS System Architecture

FIG. 20 is a system diagram illustrating a DFS system architecture inwhich an embodiment may be implemented. The DFS system 200 includes adata intake and query system 202 communicatively coupled to a network ofdistributed components that collectively form a big data ecosystem. Thedata intake and query system 202 may include the components of dataintake and query systems discussed above including any combination offorwarders, indexers, data stores, and a search head. However, the dataintake and query system 202 is illustrated with fewer components to aidin understanding how the disclosed embodiments extend the capabilitiesof data intake and query systems to apply search queries and analyticsoperations on distributed data systems including internal data systems(e.g., indexers with associated data stores) and/or external datasystems in a big data ecosystem.

The data intake and query system 202 includes a search head 204communicatively coupled to multiple peer indexers 206 (also referred toindividually as indexer 206). Each indexer 206 is responsible forstoring and searching a subset of events contained in a correspondingdata store (not shown). The peer indexers 206 can analyze events for asearch query in parallel. For example, each indexer 206 can returnpartial results in response to a search query as applied by the searchhead 204.

The disclosed technique expands the capabilities of the data intake andquery system 202 to obtain and harmonize search results from externaldata sources 208, alone or in combination with the partial searchresults of the indexers 206. More specifically, the data intake andquery system 202 runs various processes to apply a search query to theindexers 206 as well as external data sources 208. For example, a daemon210 of the data intake and query system 202 can operate as a backgroundprocess that coordinates the application of a search query on theindexers and/or the external data stores. As shown, the daemon 210includes software components for the search head 204 and indexers 206 tointerface with a DFS master 212 and a distributed network of workernodes 214, respectively, which are external to the data intake and querysystem 202.

The DFS master 212 is communicatively coupled to the search head 204 viathe daemon 210-3. In some embodiments, the DFS master 212 can includesoftware components running on a device of any system, including thedata intake and query system 202. As such, the DFS master 212 caninclude software and underlying logic for establishing a logicalconnection to the search head 204 when external data systems need to besearched. The DFS master 212 is part of the DFS search service (“searchservice”) that includes a search service provider 216, which interfaceswith the worker nodes 214.

Although shown as separate components, the DFS master 212 and the searchservice provider 216 are components of the search service that mayreside on the same machine, or may be distributed across multiplemachines. In some embodiments, running the DFS master 212 and the searchservice provider 216 on the same machine can increase performance of theDFS system by reducing communications over networks. As such, the searchhead 204 can interact with the search service residing on the samemachine or on different machines. For example, the search head 204 candispatch requests for search queries to the DFS master 212, which canspawn search service providers 216 of the search service for each searchquery.

Other functions of the search service provider 216 can include providingdata isolation across different searches based on role/access control,as well as fault tolerance (e.g., localized to a search head). Forexample, if a search operation fails, then its spawned search serviceprovider may fail but other search service providers for other searchescan continue to operate.

The search head 204 can define a search scheme in response to a receivedsearch query that requires searching both the indexers 206 and theexternal data sources 208. A portion of the search scheme can be appliedby the search head 204 to the indexers 206 and another portion of thesearch scheme can be communicated to the DFS master 212 for applicationby the worker nodes 214 to the external data sources 208. The searchservice provider 216 can collect an aggregate of partial search resultsof the indexers 206 and of the external data sources 208 from the workernodes 214, and communicate the aggregate partial search results to thesearch head 204. In some embodiments, the DFS master 212, search head204, or the worker nodes 214 can produce the final search results, whichthe search head 204 can cause to be presented on a user interface of adisplay device.

More specifically, the worker nodes 214 can act as agents of the DFSmaster 212 via the search service provider 216, which can act on behalfof the search head 204 to apply a search query to distributed datasystems. For example, the DFS master 212 can manage different searchoperations and balance workloads in the DFS system 200 by keeping trackof resource utilization while the search service provider 216 isresponsible for executing search operations and obtaining the searchresults.

For example, the search service provider 216 can cause the worker nodes214 to apply a search query to the external data sources 208. The searchservice provider 216 can also cause the worker nodes 214 to collect thepartial search results from the indexers 206 and/or the external datasources 208 over the computer network. Moreover, the search serviceprovider 216 can cause the worker nodes 214 to aggregate the partialsearch results collected from the indexers 206 and/or the external datasources 208.

Hence, the search head 204 can offload at least some processing to theworker nodes 214 because the distributed worker nodes 214 can extractpartial search results from the external data sources 208, and collectthe partial search results of the indexers 206 and the external datasources 208. Moreover, the worker nodes 214 can aggregate the partialsearch results collected from the diverse data systems and transfer themto the search service, which can finalize the search results and sendthem to the search head 204. Aggregating the partial search results ofthe diverse data systems can include combining partial search results,arranging the partial search results in an ordered manner, and/orperforming operations derive other search results from the collectedpartial search results (e.g., transform the partial search results).

Once a logical connection is established between the search head 204,the DFS master 212, the search service provider 216, and the workernodes 214, control and data flows can traverse the components of the DFSsystem 200. For example, the control flow can include instructions fromthe DFS master 212 to the worker nodes 214 to carry out the operationsdetailed further below. Moreover, the data flow can include aggregatepartial search results transferred to the search service provider 216from the worker nodes 214. Further, the partial search results of theindexers 206 can be transferred by peer indexers to the worker nodes 214in accordance with a parallel export technique. A more detaileddescription of the control flow, data flow, and parallel exporttechniques are provided further below.

In some embodiments, the DFS system 200 can use a redistribute operatorof a data intake and query system. The redistribute operator candistribute data in a sharded manner to the different worker nodes 214.Use of the redistribute operator may be more efficient than the parallelexporting because it is closely coupled to the existing data intake andquery system. However, the parallel exporting techniques havecapabilities to interoperate with open source systems other than theworker nodes 214. Hence, use of the redistribute operator can providegreater efficiency but less interoperability and flexibility compared tousing parallel export techniques.

The worker nodes 214 can be communicatively coupled to each other, andto the external data sources 208. Each worker node 214 can include oneor more software components or modules 218 (“modules”) operable to carryout the functions of the DFS system 200 by communicating with the searchservice provider 216, the indexers 206, and the external data sources208. The modules 218 can run on a programming interface of the workernodes 214. An example of such an interface is APACHE SPARK, which is anopen source computing framework that can be used to execute the workernodes 214 with implicit parallelism and fault-tolerance.

In particular, SPARK includes an application programming interface (API)centered on a data structure called a resilient distributed dataset(RDD), which is a read-only multiset of data items distributed over acluster of machines (e.g., the devices running the worker nodes 214).The RDDs function as a working set for distributed programs that offer aform of distributed shared memory. The RDDs facilitate implementation ofboth iterative algorithms, to read their dataset multiple times in aloop, and interactive or exploratory data analysis (e.g., for repeateddatabase-style querying of data).

Thus, the DFS master 212 can act as a manager of the worker nodes 214,including their distributed data storage systems, to extract, collect,and store partial search results via their modules 218 running on acomputing framework such as SPARK. However, the embodiments disclosedherein are not limited to an implementation that uses SPARK. Instead,any open source or proprietary computing framework running on acomputing device that facilitates iterative, interactive, and/orexploratory data analysis coordinated with other computing devices canbe employed to run the modules 218 for the DFS master 212 to applysearch queries to the distributed data systems.

Accordingly, the worker nodes 214 can harmonize the partial searchresults of a distributed network of data storage systems, and providethose aggregated partial search results to the search service provider216. In some embodiments, the DFS master 212 can further operate on theaggregated partial search results to obtain final results that arecommunicated to the search head 204, which can output the search resultsas reports or visualizations on a display device.

The DFS system 200 is scalable to accommodate any number of worker nodes214. As such, the DFS system can scale to accommodate any number ofdistributed data systems upon which a search query can be applied andthe search results can be returned to the search head and presented in aconcise or comprehensive way for an analyst to obtain insights into biddata that is greater in scope and provides deeper insights compared toexisting systems.

4.2. DFS System Operations

FIG. 21 is an operation flow diagram illustrating an example of anoperation flow of the DFS system 200. The operation flow 2100 includescontrol flows and data flows of the data intake and query system 202,the DFS master 212 and/or the search service provider 216 (collectivelythe “search service 220”), one or more worker nodes 214, and/or one ormore external data sources 208. A combination of the search service 220and the worker nodes 216 collectively enable the data fabric servicesthat can be implemented on the distributed data systems including, forexample, the data intake and query system 202 and the external datasources 208.

In step 2102, the search head 204 of the data intake and query system202 receives a search query. For example, an analyst may submit a searchquery to the search head 204 over a network from an application (e.g.,web browser) running on a client device, through a network portal (e.g.,website) administered by the data intake and query system 202. Inanother example, the search head 204 may receive the search query inaccordance with a schedule of search queries. The search query can beexpressed in a variety of languages such as a pipeline search language,a structured query language, etc.

In step 2104, the search head 204 processes the search query todetermine whether the search query requires searching the indexers 206,the external data sources 208, or a combination of both. For example,the scope of a search query may be limited to searching only theindexers 206. If so, the search head 204 can conduct a search on theindexers 206 by using, for example, map-reduce techniques withoutinvoking or engaging the DFS system. In some embodiments, however, thesearch head 204 can invoke or engage the DFS system to utilize theworker nodes 214 to harmonize the partial search results of the indexers206 alone, and return the search results to the search head 204 via thesearch service 220.

If, on the other hand, the scope of the search query requires searchingat least one external data system, then the search head 204 can invokeand engage the DFS system. Accordingly, the search head 204 can engagethe search service 220 when a search query must be applied to at leastone external data system, such as a combination of the indexers 206 andat least one of the external data sources 208. For example, the searchhead 204 can receive a search query, and create search phases inresponse to that query. The search head can pass search phases to theDFS master 212, which can create (e.g., spawn) a search service provider(e.g., search service provider 216) to conduct the search.

In some embodiments, the DFS system 200 can be launched by using amodular input, which refers to a platform add-on of the data intake andquery system 202 that can be accessed in a variety of ways such as, forexample, over the Internet on a network portal. For example, the searchhead 204 can use a modular input to launch the search service 220 andworker nodes 214 of the DFS system 200. In some embodiments, a modularinput can be used to launch a monitor function used to monitor nodes ofthe DFS system. In the event that a launched service or node fails, themonitor allows the search head to detect the failed service or node, andre-launch the failed service or node or launch or reuse another launchedservice or node to provide the functions of the failed service or node.

In step 2104, the data intake and query system 202 executes a searchphase generation process to define a search scheme based on the scope ofthe search query. The search phase generation process involves anevaluation of the scope of the search query to define one or more phasesto be executed by the data intake and query system 202 and/or the DFSsystem, to obtain search results that would satisfy the search query.The search phases may include a combination of phases for initiatingsearch operations, searching the indexers 206, searching the externaldata sources 208, and/or finalizing search results for return back tothe search head 204.

In some embodiments, the combination of search phases can include phasesfor operating on the partial search results retrieved from the indexers206 and/or the external data sources 208. For example, a search phasemay require correlating or combining partial search results of theindexers 206 and/or the external data sources 208. In some embodiments,a combination of phases may be ordered as a sequence that requires anearlier phase to be completed before a subsequent phase can begin.However, the disclosure is not limited to any combination or order ofsearch phases. Instead, a search scheme can include any number of searchphases arranged in any order that could be different from another searchscheme applied to the same or another arrangement or subset of datasystems.

For example, a first search phase may be executed by the search head 204to extract partial search results from the indexers 206. A second searchphase may be executed by the worker nodes 214 to extract and collectpartial search results from the external data sources 208. A thirdsearch phase may be executed by the indexers 206 and worker nodes 214 toexport partial search results in parallel to the worker nodes 214 fromthe (peer) indexers 206. As such, the third phase involves collectingthe partial search results from the indexers 206 by the worker nodes214. A fourth search phase may be executed by the worker nodes 214 toaggregate (e.g., combine and/or operate on) the partial search resultsof the indexers 206 and/or the worker nodes 214. A sixth and seventhphase may involve transmitting the aggregate partial search results tothe search service 220, and operating on the aggregate partial searchresults to produce final search results, respectively. The searchresults can then be transmitted to the search head 204. An eight searchphase may involve further operating on the search results by the searchhead 204 to obtain final search results that can be, for example,rendered on a user interface of a display device.

In step 2106, the search head 204 initiates a communications searchprotocol that establishes a logical connection with the worker nodes 214via the search service 220. Specifically, the search head 204 maycommunicate information to the search service 220 including a portion ofthe search scheme to be performed by the worker nodes 214. For example,a portion of the search scheme transmitted to the DFS master 212 mayinclude search phase(s) to be performed by the DFS master 212 and theworker nodes 214. The information may also include specific controlinformation enabling the worker nodes 214 to access the indexers 206 aswell as the external data sources 208 subject to the search query.

In step 2108, the search service 220 can define an executable searchprocess performed by the DFS system. For example, the DFS master 212 orthe search service provider 216 can define a search process as a logicaldirected acyclic graph (DAG) based on the search phases included in theportion of the search scheme received from the search head 204.

The DAG includes a finite number of vertices and edges, with each edgedirected from one vertex to another, such that there is no way to startat any vertex and follow a consistently-directed sequence of edges thateventually loops back to the same vertex. Here, the DAG can be adirected graph that defines a topological ordering of the search phasesperformed by the DFS system. As such, a sequence of the verticesrepresents a sequence of search phases such that every edge is directedfrom earlier to later in the sequence of search phases. For example, theDAG may be defined based on a search string for each phase or metadataassociated with a search string. The metadata may be indicative of anordering of the search phases such as, for example, whether results ofany search string depend on results of another search string such thatthe later search string must follow the former search stringsequentially in the DAG.

In step 2110, the search head 204 starts executing local search phasesthat operate on the indexers 206 if the search query requires doing so.If the scope of the search query requires searching at least oneexternal data system, then, in step 2112, the search head 204 sendsinformation to the DFS master 212 triggering execution of the executablesearch process defined in step 2108.

In step 2114, the search service 220 starts executing the search phasesthat cause the worker nodes 214 to extract partial search results fromthe external data stores 208 and collect the extracted partial searchresults at the worker nodes 214, respectively. For example, the searchservice 220 can start executing the search phases of the DAG that causethe worker nodes 214 to search the external data sources 208. Then, instep 2116, the worker nodes 214 collect the partial search resultsextracted from the external data sources 208.

The search phases executed by the DFS system can also cause the workernodes 214 to communicate with the indexers 206. For example, in step2118, the search head 204 can commence a search phase that triggers aremote pipeline executed on the indexers 206 to export their partialsearch results to the worker nodes 214. As such, the worker nodes 214can collect the partial search results of the indexers 206. However, ifthe search query does not require searching the indexers 206, then thesearch head 204 may bypass triggering the pipeline of partial searchresults from the indexers 206.

In step 2122, the worker nodes 214 can aggregate the partial searchresults and send them to the search service 220. For example, the searchservice provider 216 can begin collecting the aggregated search resultsfrom the worker nodes 214. The aggregation of the partial search resultsmay including combining the partial search results of indexers 206, theexternal data stores 208, or both. In some embodiments, the aggregatedpartial search results can be time-ordered or unordered depending on therequirements of the type of search query.

In some embodiments, aggregation of the partial search results mayinvolve performing one or more operations on a combination of partialsearch results. For example, the worker nodes 214 may operate on acombination of partial search results with an operator to output a valuederived from the combination of partial search results. Thistransformation may be required by the search query. For example, thesearch query may be an average or count of data events that includespecific keywords. In another example, the transformation may involvedetermining a correlation among data from different data sources thathave a common keyword. As such, transforming the search results mayinvolve creating new data derived from the partial search resultsobtained from the indexers 206 and/or external data systems 208.

In step 2124, a data pipeline is formed to the search head 204 throughthe search service 220 once the worker nodes 214 have ingested thepartial search results from the indexers 206 and the external datastores 208, and aggregated the partial search results (e.g., andtransformed the partial search results).

In step 2126, the aggregate search received by the search service 220may optionally be operated on to produce final search results. Forexample, the aggregate search results may include different statisticalvalues of partial search results collected from different worker nodes214. The search service 220 may operate on those statistical values toproduce search results that reflect statistical values of thestatistical values obtained from the all the worker nodes 214.

As such, the produced search results can be transferred in a big datapipeline to the search head 204. The big data pipeline is essentially apipeline of the data intake and query system 202 extended into the bigdata ecosystem. Hence, the search results are transmitting to the searchhead 204 where the search query was received by a user. Lastly, in step2128, the search head 204 can render the search results or dataindicative of the search results on a display device. For example, thesearch head 204 can make the search results available for visualizing ona user interface rendered via a computer portal.

5.0. Parallel Export Techniques

The disclosed embodiments include techniques for exporting partialsearch results in parallel from peer indexers of a data intake and querysystem to the worker nodes. In particular, partial search results (e.g.,time-indexed events) obtained from peer indexers can be exported inparallel from the peer indexers to worker nodes. Exporting the partialsearch results from the peer indexers in parallel can improve the rateat which the partial search results are transferred to the worker nodesfor subsequent combination with partial search results of the externaldata systems. As such, the rate at which the search results of a searchquery can be obtained from the distributed data system can be improvedby implementing parallel export techniques.

FIG. 22 is an operation flow diagram illustrating an example of aparallel export operation performed in a DFS system according to someembodiments of the present disclosure. The operation 2200 for parallelexporting of partial search results from peer indexers 206 begins byprocessing a search query that requires transferring of partial searchresults from the peer indexers 206 to the worker nodes 214.

In step 2202, the search head 204 receives a search query as, forexample, input by a user of a client device. In step 2204, the searchhead 204 processes the search query to determine whether internal datastores 222 of peer indexers 206 must be searched for partial searchresults. If so, in step 2206, the search head 204 executes a process tosearch the peer indexers 206 and retrieve the partial search results. Instep 2208, each peer indexer 206 can return its partial search resultsretrieved from respective internal data stores 222.

In step 2210, the partial search results (e.g., time-indexed events)obtained by the peer indexers 206 can be sharded into chunks of events(“event chunks”). Sharding involves partitioning large data sets intosmaller, faster, more easily managed parts called data shards. Thesharded partitions can be determined from policies, which can be basedon hash values by default. Accordingly, the retrieved events can begrouped into chunks (i.e., micro-batches) based on a value associatedwith a search query and/or the corresponding retrieved events. Forexample, the retrieved events can be sharded in chunks based on thefield names passed as part of a search query process of the data intakeand query system. The event chunks can then be exported from the peerindexers 206 in parallel over the network to the worker nodes 214.

If time-ordering is required, the parallel exporting technique caninclude a mechanism to reconstruct the ordering of event chunks at theworker nodes 214. In particular, the order from which the event chunksflowed from peer indexers 206 can be tracked to enable collating thechunks in time order at the worker nodes 214. For example, metadata ofevent chunks can be preserved when parallel exporting such that thechunks can be collated by the worker nodes 214 that receive the eventchunks. Examples of the metadata include SearchResultsInfo (SRI) (a datastructure of SPLUNK® which carries control and meta information for thesearch operations) or timestamps indicative of, for example, the timeswhen respective events or event chunks started flowing out from the peerindexers 206. If time ordering is not required, preserving the timeordering of chunks by using timestamps may be unnecessary.

The parallel exporting technique can be modified in a variety of ways toimprove performance of the DFS system. For example, in step 2214, theevent chunks can be load balanced across the peer indexers 206 and/orreceiving worker nodes 214 to improve network efficiency and utilizationof network resources. In particular, a dynamic list of receivers (e.g.,worker nodes 214) can be maintained by software running on hardwareimplementing the DFS system. The list may indicate a currentavailability of worker nodes to receive chunks from export processors ofthe peer indexers 206. The list can be updated dynamically to reflectthe availability of the worker nodes 214. Further, parameters on thelist indicative of the availability of the worker nodes 214 can bepassed to the export processers periodically or upon the occurrence ofan event (e.g., a worker node 214 becomes available). The exportprocessers can then perform a load balancing operation on the eventchunks over the receiving worker nodes 214.

The worker nodes 214 may include driver programs that consume the eventsand event chunks. In some embodiments, the worker nodes 214 can includea software development kit (SDK) that allows third party developers tocontrol the consumption of events from the peer indexers 206 by theworker nodes 214. As such, third party developers can control thedrivers causing the consumption of events and event chunks from the peerindexers 206 by the worker nodes 214. Lastly, in step 2216, the eventchunks are exported from the peer indexers 206 in parallel to the workernodes 214.

In some embodiments, the rate of exporting events or event chunks inparallel by the peer indexers 206 can be based on an amount of sharedmemory available to the worker nodes 214. Accordingly, techniques can beemployed to reduce the amount of memory required to store transferredevents. For example, when the worker nodes 214 are not local (e.g.,remote from the peer indexers 206), compressed payloads of the eventchunks can be transferred to improve performance.

Thus, the disclosed DFS system can provide a big data pipeline andnative processor as a mechanism to execute infrastructure, analytics,and domain-based processors based on data from one or more external datasources over different compute engines. In addition, the mechanism canexecute parallelized queries to extract results from external systems.

6.0. DFS Query Processing

The disclosed embodiments include techniques to process search queriesin different ways by the DFS system depending on the type of searchresults sought in response to a search query. In other words, a dataintake and query system can receive search queries that cause the DFSsystem to process the search queries differently based on the searchresults sought in accordance with the search queries. For example, somesearch queries may require ordered search results, and an order of thesearch results may be unimportant for other search queries.

To obtain ordered search results, a search query executed on internaldata sources (e.g., indexers) and/or external data sources may requiresorting and organizing timestamped partial search results across themultiple diverse data sources. However, the multiple internal orexternal data sources may not store timestamped data. That is, some datasources may store time-ordered data while other data sources may notstore time-ordered data, which prevents returning time-ordered searchresults for a search query. The disclosed embodiments provide techniquesfor harmonizing time-ordered and unordered data from across multipleinternal or external data sources to provide time-ordered searchresults.

In other instances, a search query may require search results thatinvolve performing a transformation of data collected from multipleinternal and/or external data sources. The transformed data can beprovided as the search results in response to the search query. In somecases, the search query may be agnostic to the ordering of the searchresults. For example, the search results of a search query may requirecounts of different types of events generated over the same period oftime. Hence, search results that satisfy the search query could beordered or unordered counts. As such, there is no requirement tomaintain the time order of the partial search results obtained from datasystems subject to the count search query. Thus, the techniquesdescribed below provide mechanisms to obtain search result from the biddata ecosystem that are transformed, time-ordered, unordered, or anycombinations of these types of search results.

6.1. Ordered Search Results

The disclosed embodiments include techniques to obtain ordered searchresults based on partial search results from across multiple diverseinternal and/or external data sources. The ordering of the searchresults may be with respect to a parameter associated with the partialsearch results. An example of a parameter includes time. As such, thedisclosed technique can provide a time-ordered search result based onpartial search results obtained from across multiple internal and/orexternal data sources. Moreover, the disclosed technique can providetime-ordered search results regardless of whether the partial searchresults obtained from the diverse data sources are timestamped.

An ordered search (i.e., ordered data execution) can be referred to as“cursored” mode of data access. According to this mode of data access,the DFS system can execute time-ordered searches or retrieve events frommultiple data sources and presents the events in a time ordered manner.For searches involving only local data sources, the DFS system canimplement a micro-batching mechanism based on the event time acrossworker nodes. The DFS system can ensure that per peer ordering isenforced across the worker nodes and final collation is performed at alocal search head. In case of event retrieval from multiple datasources, the DFS system can maintain per source ordering prior toordered collation in the local search head.

FIG. 23 is a flowchart illustrating a method performed the DFS system toobtain time-ordered search results in response to a cursored searchquery according to some embodiments of the present disclosure. Asdescribed below, the method 2300 for processing cursored search queriescan involve a micro-batching process executed by worker nodes to ensuretime orderliness of partial search results obtained from data sources.

In step 2302, one or more worker nodes collect partial search resultsfrom the internal and/or external data sources. For example, the workernode may collect partial search results corresponding to data having adata structure as specified by the search query. In another example, theworker nodes may query an external data source for partial searchresults based on specific keywords specified by a cursored search query,and collect the partial search results. The worker nodes may alsocollect partial search results from indexers, which were returned inresponse to application of the search query by the search head to theindexers. In some embodiments, the partial search results may becommunicated from each data source to the worker node in chunks (i.e.,micro-batches).

In step 2304, the worker nodes perform deserialization of the partialsearch results obtained from the data sources. Specifically, partialsearch results transmitted by the data sources could been serializedsuch that data objects were converted into a stream of bytes in order totransmit the object, or store the object in memory. The serializationprocess allows for saving the state of an object in order to reconstructit at the worker node by using reverse process of deserialization.

In step 2306, the worker nodes ingest the partial search resultscollected from the data sources and transform them into a specifiedformat. As such, partial search results in diverse formats can betransformed into a common specified format. The specified format may bespecified to facilitate processing by the worker nodes. Hence, diversedata types obtained from diverse data sources can be transformed into acommon format to facilitate subsequent aggregation across all thepartial search results obtained in response to the search query. As aresult, the partial search results obtained by the worker nodes can betransformed into, for example, data events having structures that arecompatible to the data intake and query system.

In step 2308, the worker nodes may determine whether the partial searchresults are associated with respective time values. For example, theworker nodes may determine that events or event chunks from an internaldata source are timestamped as shown in FIG. 2, but events or eventchunks from an external data source may not be timestamped. Thetimestamped events may also be marked with an “OriginType” (e.g.,mysql-origin, cloud-aws-s), “SourceType” (e.g., cvs, json, sql), and“Host < >” (e.g., IP address where the event originated), or other datauseful for ordering the partial search. If all the partial searchresults from across the diverse data systems are adequately marked, thenharmonizing the partial search results may not require different typesof processing. However, typically at least some partial search resultsfrom across the diverse distributed data systems are not adequatelymarked to facilitate harmonization.

Accordingly, the worker nodes can implement bifurcate processing of thepartial search results depending on whether or not the partial searchresults are adequately marked. Specifically, the partial search resultsthat are timestamped can be processed one way, and the partial searchresults that are not timestamped can be processed a different way. Theworker nodes can execute the different types of processinginterchangeably, or execute one type of processing after the other typeof processing has completed.

In step 2310, for time-ordered partial search results, respective workernodes can be assigned (e.g., fixed) to receive time-ordered partialsearch results (e.g., events or event chunks) from respective datasources in an effort to maintain the time orderliness of the data.Assigning a worker node to obtain time-ordered partial search results ofthe same data source avoids the need for additional processing amongmultiple nodes otherwise required if they each received differenttime-ordered chunks from the same data source. In other words, setting aworker node to collect all the time-ordered partial search results fromits source avoids the added need to distribute the time-ordered partialsearch results between worker nodes to reconstruct the overall timeorderliness of the partial search results.

For example, a worker node can respond to timestamped partial searchresults it receives by setting itself (or another worker node) toreceive all of the partial search results from the source of thetime-stamped partial search results. For example, the worker node can beset by broadcasting the assignment to other worker nodes, whichcollectively maintain a list of assigned worker nodes and data sources.In some embodiments, a worker node that receives timestamped partialsearch results can communicate an indication about the timestampedpartial search results to the DFS master. Then the DFS master can set aspecific set of worker nodes to receive all the timestamped data fromthe specific source.

In step 2312, the worker nodes read the collected partial search results(e.g., events or event chunks) and arrange the partial search results intime order. For example, each collected event or event chunk may beassociated with any combination of a start time, an end time, a creationtime, or some other time value. The worker node can use the time values(e.g., timestamps) associated with the events or event chunks to arrangethe events and/or the event chunks in a time-order. Lastly, in step2314, the worker nodes may stream the time-ordered partial searchresults in parallel as time-ordered chunks via the search service (e.g.,to the DFS master via the service provider of the DFS system).

Referring back to step 2308, the worker nodes can respond differently topartials search results that are not associated with timestamps (e.g.,lack an associated time value that facilitates time ordering). In step2316, the worker nodes can associate events or chunks with a time valueindicative of the time of ingestion of the events or event chunks by therespective worker nodes (e.g., an ingestion timestamp). The worker nodescan associate the partial search results with any time value that can bemeasured relative to a reference time value (i.e., not limited to aningestion timestamp). In some embodiments, the partial search resultstimestamped by the worker nodes can also be marked with a flag todistinguish those partial search results from the partial search resultsthat were timestamped before being collected by the worker nodes.

In step 2318, the worker nodes sort the newly timestamped partial searchresults and create chunks (e.g., micro-batches) upon completion ofcollecting all of the partial search results from the data sources. Insome embodiments, the chunks may be created to contain a default minimumor maximum number of partial search results (i.e., a default chunksize). As such, the worker nodes can create time-ordered partial searchresults obtained from data sources that did not provide time-orderedpartial search results.

In step 2320, the worker nodes can apply spillover techniques to disk asneeded. In some embodiments, the worker nodes can provide an extensiveHB/status update mechanism to notify the DFS master of its currentblocked state. In some embodiments, the worker nodes can ensure akeep-alive to override timeout and provide notifications. Lastly, instep 2322, the worker nodes may stream the time-ordered partial searchresults in parallel as time-ordered chunks via the search service (e.g.,to the DFS master via the service provider of the DFS system).

Accordingly, time-ordered partial search results can be created from acombination of time-ordered and non-time-ordered partial searchcollected from diverse data sources. The time-ordered partial searchresults can be streamed in parallel from multiple worker nodes to theservice provider, which can stream each search stream to the search headof the data intake and query system. As such, time-ordered searchresults can be produced from diverse data types of diverse data systemswhen the scope of a search query requires doing so.

FIG. 24 is a flowchart illustrating a method performed by a data intakeand query system of a DFS system in response to a cursored search queryaccording to some embodiments of the present disclosure. Specifically,the method 2400 can be performed by the data intake and query system tocollate the time-ordered partial search results obtained by queryinginternal and/or external data sources.

In step 2402, the search head of the data intake and query systemreceives one or more streams of time-ordered partial search results(e.g., event chunks) from a search service. In step 2404, the searchhead creates multiple search collectors to collect the time orderedevent chunks.

For example, the search head can add a class of collectors to collatesearch results from the worker nodes. In some embodiments, the searchhead can create multiple collectors; such as a collector for eachindexer, as well as a single collector for each external data source. Insome embodiments, the search head may create a collector for eachstream, which could include time-ordered chunks from a single workernode or a single data source. Hence, each collector receivestime-ordered chunks.

In step 2406, the collectors perform a deserialization process on thereceived chunks and their contents, which had been serialized fortransmission from the search service. In step 2408, each collector addsthe de-serialized partial search or their chunks to a collector queue.The search head may include any number of collector queues. For example,the search head may include a collector queue for each collector or foreach data source that provided partial search results.

In step 2410, the search head can collate the time-ordered partialsearch results obtained from the internal and/or external data sourcesas time-ordered search results of the presented search query. Forexample, the search head may apply a collation operation based on thetime-order of events contained in the chunks from the queues ofdifferent collectors to provide time-ordered search results.

Lastly, in step 2412, the time-ordered search results could be providedto an analyst on a variety of mediums and in a variety of formats. Forexample, the time-ordered search results may be rendered as a timelinevisualization on a user interface on a display device. In someembodiments, the raw search results (e.g., entire raw events) areprovided for the timeline visualization.

The visualization can allow the analyst to investigate the searchresults. In another example, the time-ordered results may be provided toan analyst automatically on printed reports, or transmitted in a messagesent over a network to a device to alert the analyst of a conditionbased on the search results.

Although the methods illustrated in FIGS. 23 and 24 include acombination of steps to obtain time-ordered search results from acrossdiverse data sources that may or may not provide timestamped data, thedisclosed embodiments are not so limited. Instead, any portion of thecombination of steps illustrated in FIGS. 23 and 24 could be performeddepending on the scope of the search query. For example, only a subsetof steps may be performed when the search results for a search query areobtained exclusively from a single external data source that storestimestamped data.

6.2. Transformed Search Results

The disclosed embodiments include a technique to obtain search resultsfrom the application of transformation operations on partial searchresults obtained from across internal and/or external data sources.Examples of transformation operations include arithmetic operations suchas an average, mean, count, or the like. Examples of reportingtransformations include join operations, statistics, sort, top head.Hence, the search results of a search query can be derived from partialsearch result rather than include the actual partial search results. Inthis case, the ordering of the search results may be nonessential. Anexample of a search query that requires a transformation operation is a“batch” or “reporting” search query. The related disclosed techniquesinvolve obtaining data stored in the bid data ecosystem, and returningthat data or data derived from that data.

According to a reporting or batch mode of data access, the DFS systemexecutes blocking transforming searches, for example, to join across oneor multiple available data sources. Since ordering is not needed, theDFS system can implement sharding of the data from the various datasources and execute aggregation (e.g., reduction of map-reduction) inparallel. The DFS architecture can also execute multiple DFS operationsin parallel to ingest sharded data from the different sources.

FIG. 25 is a flowchart illustrating a method performed by nodes of a DFSsystem to obtain search results in response to a batch or reportingsearch query according to some embodiments of the present disclosure.The method 2500 for processing batch or reporting search queries caninvolve steps performed by the DFS master, the service provider, and/orworker nodes to transform partial search results into search resultsinto batch or reporting search results. The disclosed techniques alsosupport both streaming and non-streaming for multiple data sources.

The transformation operations generally occur at the worker nodes. Forexample, an operation may include a statistical count of events having aparticular IP address. The DFS can shard the data in certain partitions,and then each worker node can apply the transformation to thatparticular partition. In case it is the last reporting/transformingprocessor, then the transformed results are collated at the serviceprovider, and then transmitted to the search head. However, if there isan reporting search beyond the statistical count, then another reshuffleof the partial search results can be executed among the worker nodes toput the different partitions on the same worker node, and thentransforms can be applied. If this is the last reporting search, thenresults are sent back to the service provide node and then to the searchhead. This process continues as dictated by the DAG generated from thephase desired by the search head.

In step 2502, the worker nodes collect partial search results from theinternal and/or external data sources. For example, a worker node maycollect partial search results including data having data structuresspecified by the search query. In another example, the worker node mayquery an external data source for partial search results based onspecific keywords included in a reporting search query, and collect thepartial search results. The worker node may also collect partial searchresults from indexers, which were returned in response to application ofthe reporting search query by the search head to the indexers. Thepartial search results may be communicated from each data source to theworker nodes individually or in chunks (i.e., micro-batches). The workernodes thus ingest partial search results obtained from the data sourcesin response to a search query.

In step 2504, the worker nodes can perform deserialization of thepartial search results obtained from the data sources. Specifically, thepartial search results transmitted by the data sources can be serializedby converting objects into a stream of bytes, which allows for savingthe state of an object for subsequent recreation of the object at theworker nodes by using the reverse process of deserialization.

In step 2506, the worker nodes transform the de-serialized partialsearch results into a specified format. As such, partial search resultscollected in diverse formats can be transformed into a common specifiedformat. The specified format may be specified to facilitate processingby a worker node. As such, diverse data types obtained from diverse datasources can be transformed into a common format to facilitate subsequentaggregation across all the partial search results obtained in responseto the search query. As a result, the partial search results obtained byworker nodes can be transformed into, for example, data events havingstructures that are compatible to the data intake and query system.

Unlike cursored search queries, the time-order of partial search resultsis not necessarily considered when processing reporting queries.However, in step 2508, if a data source returns partial search resultsthat are not associated with time values (e.g., no timestamp), theworker nodes can associate events or event chunks with a time valueindicative of the time of ingestion of the events or chunks by theworker nodes (e.g., ingestion timestamp). In some embodiments, theworker nodes can associate the partial search results with any timevalue that can be measured relative to a reference time value.Associating time values with partial search results may facilitatetracking partial search results when processing reporting searches, ormay be necessary when performing reporting searches that requiretime-ordered results (e.g., a hybrid of cursored and reportingsearches).

In step 2510, the worker nodes determine whether the ingested partialsearch results were obtained by an internal data source or an externaldata source to bifurcate processing respectively. In other words, theworker nodes process the ingested partial search results differentlydepending on whether they were obtained from an internal data source(e.g., indexers) or an external data source, if needed. That is, thiscan be the case only when reporting searches are run in the indexers;however, if all the processors in the indexers are streaming, then noprocessing unique to the indexer data is needed. However, data fromexternal data sources can be sanitized in terms of coding, timestamped,and throttles based on the timestamp.

In step 2512, for internal data sources, the worker nodes read thepartial search results obtained from indexers of a data intake and querysystem in a sharded way. In particular, the worker nodes may use a listidentifying indexers from which to pull the sharded partial searchresults. As discussed above, sharding involves partitioning datasetsinto smaller, faster, and more manageable parts called data shards. Thesharded partitions can be determined from policies, which can be basedon hash values by default. In the context of map-reduce techniques, themap step can be determined by the sharding and a predicate passed, whichmaps records matching the predicate to whatever is needed as the searchresult. The reduce step involves the aggregation of the shards. Theresults of a query are those items for which the predicate returns true.

In step 2514, the partial search results of the indexers are aggregated(e.g., combined and/or transformed) by the worker nodes. In particular,the partial search results can be in a pre-streaming format(semi-reduced), and need to be aggregated (e.g., reduced or combined)prior to aggregation with partial search results of external datasources. In step 2516, the aggregated partial search results of theindexers are aggregated (e.g., combined and/or transformed) with thepartial search results obtained from external data sources. Lastly, instep 2518, the aggregated partial search results of internal andexternal data stores can be transmitted from the worker nodes inparallel search service (e.g., to the DFS master via a search serviceprovider of the DFS system).

In step 2520, for external data sources, the worker nodes pushpredicates for the reporting search query to the external data sources.A predicate is a function that takes an argument, and returns a Booleanvalue indicating of true or false. The predicate can be passed as aquery expression including candidate items, which can be evaluated toreturn a true or false value for each candidate item.

In step 2522, the network nodes can determine whether the external datasources may or may not be able to execute a sharded query. In step 2526,for an external data source that can execute a sharded query, the workernode reads the results in different shards. In some embodiments, the DFSmaster randomly chooses which worker nodes will execute the shards. Instep 2524, for an external data sources that cannot execute a shardedquery, a worker node has the ability to spillover to disk, andredistribute to other worker nodes.

In step 2528, the worker nodes can apply an aggregation (e.g., (e.g.,combine and/or transform) or stream processing to have the partialsearch results ready for further processing against results from partialsearch results from the internal sources. Thus, referring back to step2516, the worker nodes aggregate the partial search results from alldata sources in response in response to the search query. For example,the worker nodes can apply a process similar to a reduction step of amap-reduce operation across all the partial search results obtained fromdiverse data sources. Then, in step 2518, the aggregate partial searchresults can be transmitted from the worker nodes in parallel to thesearch service provider 216. In particular, the service provider, cancollect all the finalized searches results from the worker nodes, andreturn the results to the search head.

FIG. 26 is a flowchart illustrating a method performed by a data intakeand query system of a DFS system in response to a batch or reportingsearch query according to some embodiments of the present disclosure. Inparticular, the method 2600 is performed by the data intake and querysystem to provide the batch or reporting search results obtained byquerying internal and/or external data sources.

In step 2602, a search head of the data intake and query system receivesthe aggregate partial search results from the service provider via ahybrid collector. The number and function of the hybrid collectors isdefined depending on the type of search executed. For example, for thetransforming search, the search head can create only one collector toreceive the final results from the search service provider and afterserialization directly pushes into the search result queue. In step2604, the search head uses an existing job pool to de-serialize searchresults, and can push the search results out. In such an operation,collation is not needed.

Lastly, in step 2606, the transformed search results could be providedto an analyst on a variety of mediums and in a variety of formats. Forexample, the time-ordered search results may be rendered as a timelinevisualization on a user interface on a display device. The visualizationcan allow the analyst to investigate the search results. In anotherexample, the time-ordered results may be provided to an analystautomatically on printed reports, or transmitted in a message sent overa network to a device to alert the analyst of a condition based on thesearch results.

Although the methods illustrated in FIGS. 23 through 26 include acombination of steps to obtain time ordered, unordered, or transformedsearch results from across multiple data sources that may or may notstore timestamped data, the disclosed embodiments are not so limited.Instead, a portion of a combination of steps illustrated in any of thesefigures could be performed depending on the scope of the search query.For example, only a subset of steps may be performed when the partialsearch results for a search query is obtained exclusively from anexternal data source.

7.0. Co-Located Deployment Architecture

The capabilities of a data intake and query system can be improved byimplementing the DFS system described above in a co-located deploymentwith the data intake and query system. For example, FIG. 27 is a systemdiagram illustrating a co-located deployment of a DFS system with thedata intake and query system in which an embodiment may be implemented.

In the illustrated embodiment, the system 224 shows only some componentsof a data intake and query system but can include other components(e.g., forwarders, internal data stores) that have been omitted forbrevity. In particular, the system 224 includes search heads 226-1 and226-2 (referred to collectively as search heads 226). The search heads226 collectively form a search head cluster 228. Although shown withonly two search heads, the cluster 228 can include any number of searchheads. Alternatively, an embodiment of the co-located deployment caninclude a single search head rather than the cluster 228.

The search heads 226 can operate alone or collectively to carry outsearch operations in the context of the co-located deployment. Forexample, a search head of the cluster 228 can operate as a leader thatorchestrates search. As shown, the search head 226-1 is a leader of thecluster 228. Any of the search heads 226 can receive search queries thatare processed collectively by the cluster 228. In some embodiments, aparticular search head can be designated to receive a search query andcoordinate the operations of some or all of the search heads of acluster 228. In some embodiments, a search head of the cluster 228 cansupport failover operations in the event that another search head of thecluster 228 fails.

The cluster 228 is coupled to N peer indexers 230. In particular, thesearch head 226-1 can be a leader of the cluster 228 that is coupled toeach of the N peer indexers 230. The system 224 can run one or moredaemons 232 that can carry out the DFS operations of the co-locateddeployment. In particular, the daemon 232-1 of the search head 226-1 iscommunicatively coupled to a DFS master 234, which coordinates controlof DFS operations. Moreover, each of the N peer indexers 230 run daemons232 communicatively coupled to respective worker nodes 236. The workernodes 236 are coupled to one or more data sources from which data can becollected as the partial search results of a search query. For example,the worker nodes 236 can collect partial search results of the indexersfrom internal data sources (not shown) and one or more of external datasources 240. Lastly, the worker nodes 236 are communicatively coupled tothe DFS master 234 to form the DFS architecture of the illustratedco-located embodiment.

7.1. Co-Located Deployment Operations

FIG. 28 is an operation flow diagram illustrating an example of anoperation flow of a co-located deployment of a DFS system with a dataintake and query system according to some embodiments of the presentdisclosure. The operational flow 2800 shows the processes forestablishing the co-located DFS system and search operations carried outin the context of the co-located deployment.

In step 2802, a search head of the cluster 228 can launch the DFS master234 and/or launch a connection to the DFS master 234. For example, asearch head can use a modular input to launch an open source DFS master234. Moreover, the search head can use the modular input to launch amonitor of the DFS master 234. The modular input can be a platformadd-on of the data intake and query system that can be accessed in avariety of ways such as, for example, over the Internet on a networkportal.

In step 2804, the peer indexers 230 can launch worker nodes 236. Forexample, each peer indexer 230 can use a modular input to launch an opensource worker node. In some embodiments, only some of the peer indexers230 launch worker nodes, which results in a topology where not all ofthe peer indexers 230 have an associated worker node. Moreover, the peerindexers 228 can use the modular input to launch a monitor of the workernodes 236.

In step 2806, the cluster 228 can launch one or more instances of a DFSservice. For example, any or each of the search heads of the cluster 228can launch an instance of the DFS service. Hence, the co-locateddeployment can launch and use multiple instances of a DFS service butneed only launch and use a single DFS master 234. In the event that alaunched DFS master fails, the lead search head using the monitoringmodular input can restart the failed DFS master. However, if the DFSmaster fails along with the lead search head, another search head can bedesignated as the cluster 228's leader and can re-launch the DFS master.

In step 2808, a search head of the cluster 228 can receive a searchquery. For example, a search query may be input by a user on a userinterface of a display device. In another example, the search query canbe input to the search head in accordance with a scheduled search.

In step 2810, a search head of the cluster 228 can initiate a DFS searchsession with the local DFS service. For example, any of the membersearch heads of the cluster 228 can receive a search query and, inresponse to the search query, a search head can initiate a DFS searchsession using an instance of the DFS service.

In step 2812, a search head of the cluster 228 triggers a distributedsearch on the peer indexers 230 if the search query requires doing so.In other words, the search query is applied on the peer indexers 230 tocollect partial search results from internal data stores (not shown).

In step 2814, the distributed search operations continue with the peerindexers 230 retrieving partial search results from internal datastores, and transporting those partial search results to the workernodes 236. In some embodiments, the internal partial search results arepartially reduced (e.g., combined), and transported by the peer indexers230 to their respective worker nodes 236 in accordance with parallelexporting techniques. In some embodiments, if each peer indexer does nothave an associated worker node, the peer indexer can transfer itspartial search results to the nearest worker node in the topology ofworker nodes. In step 2816, the worker nodes 236 collect the partialsearch results extracted from the external data sources 240.

In step 2818, the worker nodes 236 can aggregate (e.g., merge andreduce) the partial search results from the internal data sources andthe external data sources 240. For example, the aggregation of thepartial search results may include combining the partial search resultsof indexers 230 and/or the external data stores 240. Hence, the workernodes 236 can aggregate the collective partial search results at scalebased on DFS native processors residing at the worker nodes 236.

In some embodiments, the aggregated partial search results can be storedin memory at worker nodes before being transferred between other workernodes to execute a multi-staged parallel aggregation operation. Onceaggregation of the partial search results has been completed (e.g.,completely reduced) at the worker node 236, the aggregated partialsearch results can be read by the DFS service running locally to thecluster 228. For example, the DFS service can commence reading theaggregated search results as event chunks.

In step 2820, the aggregate partial search results read by the DFSservice are transferred to the DFS master 234. Then, in step 2822, theDFS master 234 can transfer the final search results to the cluster 228.For example, the aggregated partial search results can be transferred bythe worker nodes 236 as event chunks at scale to the DFS master 234,which can transfer search results (e.g., those received or derivedtherefrom) to the lead search head orchestrating the DFS session.

Lastly, in step 2822, a search head can cause the search results or dataindicative of the search results to be rendered on user interface of adisplay device. For example, the search head member can make the searchresults available for visualizing on a user interface rendered on thedisplay device.

8.0. Cloud Deployment Architecture

The performance and flexibility of a data intake and query system havingcapabilities extended by a DFS system can be improved with deployment ona cloud computing platform. For example, FIG. 29 is a cloud-based systemdiagram illustrating a cloud deployment of a DFS system in which anembodiment may be implemented.

In particular, a cloud computing platform can share processing resourcesand data in a multi-tenant network. As such, the platform's computingservices can be used on demand in a cloud deployment of a DFS system.The platform's ubiquitous, on-demand access to a shared pool ofconfigurable computing resources (e.g., networks, servers, storage,applications, and services), which can be rapidly provisioned andreleased with minimal effort, can be used to improve the performance andflexibility of a data intake and query system extended by a DFS system.

In the illustrated embodiment, a cloud-based system 242 includescomponents of a data intake and query system extended by the DFS systemimplemented on a cloud computing platform. However, the cloud-basedsystem 242 is shown with only some components of a data intake and querysystem in a cloud deployment but can include other components (e.g.,forwarders) that have been omitted for brevity. As such, the componentsof the cloud-based system 242 can be understood by analogy to otherembodiments described elsewhere in this disclosure.

An example of a suitable cloud computing platform include Amazon webservices (AWS), which includes elastic MapReduce (EMR) web services.However, the disclosed embodiments are not so limited. Instead, thecloud-based system 242 could include any cloud computing platform thatuses EMR-like clusters (“EMR clusters”).

In particular, the cloud-based system 242 includes a search head 244 asa tenant of a cloud computing platform. Although shown with only thesearch head 244, the cloud-based system 242 can include any number ofsearch heads that act independently or collectively in a cluster. Thesearch head 244 and other components of the cloud-based system 242 canbe configured on the cloud computing platform.

The cloud-based system 242 also includes any number of worker nodes 246as cloud instances (“cloud worker nodes 246”). The cloud worker nodes246 can include software modules 248 running on hardware devices of acloud computing platform. The software modules 248 of the cloud workernodes 246 are communicatively coupled to a search service (e.g.,including a DFS master 250), which is communicatively coupled to adaemon 252 of the search head 244 to collectively carry out operationsof the cloud-based system 242.

The cloud-based system 242 includes index cache components 254. Theindex cache components 254 are communicatively coupled to cloud storage256, which can form a global index 258. The index cache components 254are analogous to indexers, and the cloud storage 256 is analogous tointernal data stores described elsewhere in this disclosure. The indexcache components 254 are communicatively coupled to the cloud workernodes 246, which can collect partial search results from the cloudstorage 256 by applying a search query to the index cache components254.

Lastly, the cloud worker nodes 246 can be communicatively coupled to oneor more external data sources 260. In some embodiments, only some of thecloud worker nodes 246 are coupled to the external data sources 260while others are only coupled to the index cache components 254. Forexample, the cloud worker nodes 246-1 and 246-3 are coupled to both theexternal data sources 260 and the index cache component 254, while thecloud worker node 246-2 is coupled to the index cache component 254-1but not the external data sources 260.

The scale of the cloud-based system 242 can be changed dynamically asneeded based on any number of metrics. For example, the scale can changebased on pricing constraints. In another example, the scale of the EMRcluster of nodes can be configured to improve the performance of searchoperations. For example, the cloud-based system 242 can scale the EMRcluster depending on the scope of a search query to improve theefficiency and performance of search processing.

In some embodiments, the EMR clusters can have access to flexible datastores such as a Hadoop distributed file system (HDFS), Amazon simplestorage services (S3), NoSQL, SQL, and custom SQL. Moreover, in someembodiments, the cloud-based system 242 can allow for a sharded query ofdata within these flexible data stores in a manner which makes scalingand aggregating partial search results (e.g., merging) most efficientwhile in place (e.g., reduces shuffling of partial search resultsbetween cloud worker nodes).

8.1. Cloud Deployment Operations

FIG. 30 is a flow diagram illustrating an example of a method performedin a cloud-based DFS system (“cloud-based system”) according to someembodiments of the present disclosure. The operations of the cloud-basedsystem are analogous to those described elsewhere in this disclosurewith reference to other embodiments and, as such, a person skilled inthe art would understand those operations in the context of a clouddeployment. Accordingly, a description of the flow diagram 3000highlights some distinctions of the cloud deployment over otherembodiments described herein.

In step 3002, the search head of the cloud-based system receives asearch query. In step 3004, the cloud-based system determines the typeof EMR cluster to use based on the scope of the received search query.For example, the cloud-based system can support two different types ofEMR clusters. In a first type scenario, a single large EMR cluster couldbe used for all search operations. In a second type scenario, subsets ofsmaller EMR clusters can be used for each type of search load. That is,a smaller subset of an EMR cluster can be used for a less complexaggregation processing of partial search results from different datasources. In some embodiments, the scale of an EMR cluster for the firstor second type can be set for each search load by a user or based on arole quota. In other words, the scale of the EMR cluster can depend onthe user submitting the search query and/or the user's designated rolein the cloud-based system.

In step 3006, the cloud-based system is dynamically scaled based on theneeds determined from the received search query. For example, the searchheads or cloud worker nodes can be scaled under the control of a searchservice to grow or shrink as needed based on the scale of the EMRcluster used to process search operations.

In step 3008, the cloud worker nodes can collect the partial searchresults from various data sources. Then, in step 3010, the cloud workernodes can aggregate the partial search results collected from thevarious data sources. Since the cloud worker nodes can scaledynamically, this allows for aggregating (e.g., merging) partial searchresults in an EMR cluster of any scale.

In step 3012, the resulting aggregated search results can be computedand reported at scale to the search head. Thus, the cloud-based systemcan ensure that data (e.g., partial search results) from diverse datasources (e.g., including time-indexed events with raw data or other typeof data) are reduced (e.g., combined) at scale on each EMR node of theEMR cluster before sending the aggregated search results to the searchhead.

The cloud-based system may include various other features that improveon the data intake and query system extended by the DFS system. Forexample, in some embodiments, the cloud-based system can collect metricswhich can allow for a heuristic determination of spikes in DFS searchrequirements. The determination can also be accelerated throughauto-scaling of the EMR clusters.

In some embodiments, the cloud-based system can allow DFS apps of thedata intake and query system to be bundled and replicated over an EMRcluster to ensure that they are executed at scale. Lastly, thecloud-based system can include mechanisms that allow user- orrole-quota-honoring based on a live synchronization between the dataintake and query system user management features and a cloud accesscontrol features.

9.0. Timeline Visualization

The disclosed embodiments include techniques for organizing andpresenting search results obtained from within a big data ecosystem viaa data intake and query system. In particular, a data intake and querysystem may cause output of the search results or data indicative of thesearch results on a display device. An example of a display device isthe client device 22 shown in FIG. 1 connected to the data intake andquery system 16 over the network 18.

For example, the data intake and query system 16 can receive a searchquery input by a user at the client device 22. The data intake and querysystem 16 can run the query on distributed data systems to obtain searchresults. The search results are then communicated to the client device22 over the network 18. The search results can be rendered in a visualway on the display of the client device 22 using items such as windows,icons, menus, and other graphics or controls.

For example, a client device can run a web browser that renders awebsite, which can grant a user access to the data intake and querysystem 16. In another example, the client device can run a dedicatedapplication that grants a user access to the data intake and querysystem 16. In either case, the client device can render a graphical userinterface (GUI), which includes components that facilitate submittingsearch queries, and facilitate interacting with and interpreting searchresults obtained by applying the submitted search queries on distributeddata systems of a big data ecosystem.

The disclosed embodiments include a timeline tool for visualizing thesearch results obtained by applying a search query to a combination ofinternal data systems and/or external data systems. The timeline toolincludes a mechanism that supports visualizing the search results byorganizing the search results in a time-ordered manner. For example, thesearch results can be organized into graphical time bins. The timelinetool can present the time bins and the search results contained in oneor more time bins. Hence, the timeline tool can be used by an analyst tovisually investigate structured or raw data events which can be ofinterest to the analyst.

The timeline mechanism supports combining timestamped andnon-timestamped search results obtained from diverse data systems topresent a visualization of the combined search results. For example, asearch query may be applied to the external data systems that each usedifferent compute resources and run different execution engines. Thetimeline mechanism can harmonize the search results from these datasystems, and a GUI rendered on a display device can present theharmonized results in a time-ordered visualization.

FIG. 31 is a flowchart illustrating a timeline mechanism that supportsrendering search results in a time-ordered visualization according tosome embodiments of the present disclosure. For example, the search headcan dictate to the DFS master whether a cursored or reporting searchshould be executed. The search service provider then receives thespecific search request, and creates a DAG accordingly. Then the DAGorchestrates the search operations performed by the worker nodes for thecursored or reporting search.

In step 3102, the search service receives an indication that a requestfor a timeline visualization was received by the data intake and querysystem. For example, a user may input a request for a timelinevisualization before, after, or when a search query is input at a clientdevice. In another example, the data intake and query systemautomatically processes time-ordered requests to visualize in a timeline

In step 3104, the search service determines whether the requestedvisualization is for the search results of a cursored search or atime-ordered reporting search. For example, a cursored search may queryindexers of the data intake and query system as well as external datastores for a combination of time ordered partial search results. Inanother example, a time-ordered reporting search may require queryingthe indexers and external data stores for a time-ordered statistic basedon the combination of time ordered partial search results.

The search results for the timeline tool can be obtained in accordancewith a “Fast,” “Smart,” or “Verbose” search mode depending on whether acursored search or a reporting search was received. In particular, acursored search supports all three modes whereas a reporting search mayonly support the Verbose mode. The Fast mode prioritizes performance ofthe search and does not return nonessential search results. This meansthat the search returns what is essential and required. The Verbose modereturns all of the field and event data it possibly can, even if thesearch takes longer to complete, and even if the search includesreporting commands. Lastly, the default Smart mode switches between theFast and Verbose modes depending on the type of search being run (e.g.,cursored or reporting).

In step 3106, if the search is a cursored search, the search servicecreates buckets for the search results obtained from distributed datasystems. The buckets are created based on a timespan value. The timespanvalue may be a default value or a value selected by a user. For example,a timespan value may be 24 hours. The buckets may each represent adistinct portion of the timespan. For example, each bucket may representa distinct hour over a time-span of 24 hours.

The number of buckets that are created may be a default value dependingon the timespan, or depending on the number of data systems from whichsearch results were collected. For example, a default number of buckets(e.g., 1,000 buckets) may be created to span a default or selectedtimespan. In another example, distinct and unique buckets are createdfor portions of the timespan. In another example, a unique bucket iscreated per data system. In yet another example, buckets are created forthe same portion of the timespan but for different data systems.

In step 3108, search results obtained by application of the search queryto the different data systems are collected into the search buckets. Forexample, each bucket can collect the partial search results fromdifferent data systems that are timestamped with values within the rangeof the bucket. As such, the buckets support the timeline visualizationby organizing the search results.

In step 3110, the search service transfers a number of search resultscontained in the buckets to the search head. However, the search servicemay need to collect all the search results from across the data systemsinto the buckets before transferring the search results to the searchhead to ensure that the timeline visualization is rendered accurately.Moreover, the search results of the bucket may be transferred from thebuckets in chronological order. For example, the contents of the bucketsrepresenting beginning of the timespan are transferred first, and thecontents of the next buckets in time are transferred next, and so on.

In some embodiments, the number of search results transferred to thesearch head from the buckets may be a default or maximum value. Forexample, the first 1,000 search results from the buckets at thebeginning of the timespan may be first transferred to the search headfirst. In some embodiments, the search service transfer a maximum numberof search results per bin to the search head. In other words, the numberof search results transferred to the search head corresponds to themaximum number that can be contained in one or more bin of the timelinevisualization. Lastly, in step 3112, the search results of the reportingsearch received by the search head from the buckets are rendered in atimeline visualization.

In step 3114, if the search is a time-ordered reporting search, thesearch service creates buckets for the search results obtained fromdistributed data systems. The buckets can be created based on the numberof shards or partitions from which the search results are collected.

In step 3116, the search results are collected from across thepartitions. For external data sources, partial search results (e.g.,treated as raw events) are collected from across the shards/partitionsin time-order and transferred to the timeline mechanism. In case ofexternal data systems which have the capability to support shardedpartitions, multiple worker nodes can request for each specific shard orpartition. If needed, each partition can be sorted based on userspecified constraints such as, for example, a time ordering constraint.For sorting purposes, sometimes instead of unique shards, the DFS systemcan provide overlapping shards. For overlapping buckets across multipledata sources, the search service may need to collect partial searchresults across the different data sources before sending search resultsto the search head.

In step 3118, the search service transfers a number of search resultscontained in the buckets to the search head. However, the search servicemay need to collect all the search results from across the data systemsinto the buckets before transferring the search results to the searchhead to ensure that the timeline visualization is rendered accurately.Moreover, the search results of the bucket may be transferred from thebuckets in chronological order. For example, the contents of the bucketsrepresenting beginning of the timespan are transferred first, and thecontents of the next buckets in time are next, and so on.

In some embodiments, the number of search results transferred to thesearch head from the buckets may be a default or maximum value. Forexample, the first 1,000 search results from the buckets at thebeginning of the timespan may be first transferred to the search headfirst. In some embodiments, the search service transfers a maximumnumber of search results per bin to the search head. In other words, thenumber of search results transferred to the search head corresponds tothe maximum number that can be contained in one or more bin of thetimeline visualization. Lastly, in step 3120, the search results of thereporting search received by the search head from the buckets arerendered in a timeline visualization.

FIG. 32 illustrates a timeline visualization rendered on a userinterface 62 in which an embodiment may be implemented. The timelinevisualization presents event data obtained in accordance with a searchquery submitted to a data intake and query system. In the illustratedembodiment, the search query is input to search field 64 using SPL, inwhich a set of inputs is operated on by a first command line, and then asubsequent command following the pipe symbol “|” operates on the resultsproduced by the first command, and so on for additional commands. Asshown, a command on the left of the pipe symbol can set the scope of thesearch, which could include external data systems. Other commands on theright of the pipe symbol (and subsequent pipe symbols) can specify afield name and/or statistical operation to perform on the data sources.

In some embodiments, the search head can implement specific mechanism into parse the SPL. The search head can determine that some portion of thesearch query needs to be executed on the worker nodes base on the scopeof the search query requiring retrieval of search results from externaldata sources. In some embodiments, the search query can include aspecific search command that triggers the search head to realize whichportion of the search query should be executed by the DFS system. As aresult, the phase generator can define the search phases, and where eachof those phases will be executed. In addition, once the phase generatordecides an operation needs to be executed by the DFS system, the searchhead can optimize to push as much of the search operation as possible,for example, first to the external data source and then to the DFSsystem. In some embodiments, only the commands not included in the DFScommand set will be executed back on the search head once the resultsare retrieved to the search head.

The timeline visualization presents multiple dimensions of data in acompact view, which reduced the cognitive burden on analysts viewing acomplex collection of data from internal and/or external data systems.That is, the timeline visualization provides a single unified view tofacilitate analysis of events stored across the big data ecosystem.Moreover, the timeline visualization includes selectable components tomanipulate the view in a manner suitable for the needs of an analyst.

The timeline visualization includes a graphic 66 that depicts a summaryof the search results in a timeline lane (e.g., in the form of rawevents), as well as a list of the specific search results 68. As shown,the timeline summary of the search results are presented as rectangularbins that are chronologically ordered and span a period of time (e.g.,Sep. 5, 2016 5:00 PM through Sep. 6, 2016 3:00 PM). The height of a binrepresents the magnitude of the quantity of events in that grouprelative to another group arranged along the timeline. As such, theheight of each bin indicates a count of events for a subset of theperiod of events relative to other counts for other bins within theperiod of time. The events in a group represented by a bin may have atimestamp value included in the range of time values of thecorresponding bin. Below the timeline summary is a listing of events ofthe search results presented in chronological order.

FIG. 33 illustrates a selected bin 68 of the timeline visualization andthe contents of the selected bin 70 according to some embodiments of thepresent disclosure. Specifically, the timeline visualization may includegraphic components that enable an analyst to investigate additionaldimensions of the search results summarized in the timeline. As shown,each bin representing a group of events may be selectable by an analyst.Selecting a bin may cause the GUI to display the specific group ofevents associated with the bin in the list below the timeline summary.Specifically, selecting a bin may cause the GUI to display the events ofthe search results that are timestamped within a range of thecorresponding group.

The timeline visualization is customizable and adaptable to presentsearch results in various convenient manners. For example, a user canchange the ordering of groups of events to obtain a differentvisualization of the same groups. In another example, a user can changethe range of the timeline to obtain a filtered visualization of thesearch results. In yet another example, a user can hide some events toobtain a sorted visualization of a subset of the search results.

In some embodiments, the activity for each data system may appear in aseparate timeline lane. If an activity start-time and duration areavailable for a particular data system, the respective timeline may showa duration interval as a horizontal bar in the lane. If a start time isavailable, the timeline visualization may render an icon of that time onthe visualization. As such, the timeline visualization can be customizedand provide interactive features to visualize search results, andcommunicate the results in dashboards and reports.

Thus, the timeline visualization can support a timeline visualization ofexternal data systems, where each external data system may operate usingdifferent compute resources and engines. For example, the timelinevisualization can depict search results obtained from one or moreexternal data systems, collated and presented in a single and seamlessvisualization. As such, the timeline visualization is a tool ofunderlying logic that facilitates investigating events obtained from anyof the external data systems, internal data systems (e.g., indexers), ora combination of both.

The underlying logic can manage and control the timeline visualizationrendered on the GUI in response to data input and search resultsobtained from within the big data ecosystem. In some embodiments, theunderlying logic is under the control and management of the data intakeand query system. As such, an analyst can interface with the data intakeand query system to use the timeline visualization. For example, thetimeline logic can cause the timeline visualization to render activitytime intervals and discrete data events obtained from various datasystem resources in internal and/or external data systems.

The underlying logic includes the search service. Since the bins mayinclude events data from multiple data systems, each bin can representan overlapping bin across multiple data systems. Accordingly, the searchservice can collect the data events across the different data systemsbefore sending them to the search head. To finalize a search operation,the search service may transmit the maximum number of events per bin orthe maximum size per bin to the search head.

In some embodiments, the underlying logic uses the search head of thedata intake and query system to collect data events from the variousdata systems that are presented on the timeline visualization. In someembodiments, the events are collected in accordance with any of themethods detailed above, and the timeline visualization is a portal forviewing the search results obtained by implementing those methods. Assuch, the collected events can have timestamps indicative of, forexample, times when the event was generated.

The timestamps can be used by the underlying logic to sort the eventsinto the bins associated with any parameter such as a time range. Forexample, the underlying logic may include numerous bins delineated byrespective chronological time ranges over a total period of time thatincludes all the bins. In some embodiments, a maximum amount of eventstransferred into the time bins could be set.

In some embodiments, the underlying logic of the timeline visualizationcan automatically create bins for a default timespan in response tocursored searches of ordered data. For example, an analyst may submit acursored search, and the underlying logic may cause the timelinevisualization to render a display for events within a default timespan.The amount and rate at which the events are transferred to the searchhead for subsequent display on the timeline visualization could varyunder the control of the underlying logic. For example, a maximum numberof events could be transferred on a per bin basis by the worker nodes tothe search head. As such, the DFS system could balance the load on thenetwork.

In some embodiments, the underlying logic of the timeline visualizationcan utilize the sharding mechanism detailed above for reporting searchesof ordered data from external data systems. Specifically, the data couldbe sharded across partitions in response to a reporting search, whereexecutors have overlapping partitions. Further, the underlying logic maycontrol the search head to collect the events data across theshards/partitions in time order for rendering on the timelinevisualization. Under either the cursored search or reporting search, theunderlying logic may impose the maximum size of total events transferredinto bins.

10.0. Monitoring and Metering Services

The disclosed embodiments also include monitoring and metering servicesof the DFS system. Specifically, these services can include techniquesfor monitoring and metering metrics of the DFS system. The metrics arestandards for measuring use or misuse of the DFS system. Examples of themetrics include data or components of the DFS system. For example, ametric can include data stored or communicated by the DFS system orcomponents of the DFS system that are used or reserved for exclusive useby customers. The metrics can be measured with respect to time orcomputing resources (e.g., CPU utilization, memory usage) of the DFSsystem. For example, a DFS service can include metering the usage ofparticular worker nodes by a customer over a threshold period of time.

In some embodiments, a DFS service can meter the amount hours that aworker node spends running one or more tasks (e.g., a search requests)for a customer. In another example, a DFS service can meter the amountof resources used to run one or more tasks rather than, or incombination with, the amount of time taken to complete the task(s). Insome embodiments, the licensing approaches include the total DFS hoursused per month billed on a per hour basis; the maximum capacity that canbe run at any one time, e.g. the total number of workers with a cap onthe amount of size of each worker defined by CPU and RAM available tothat worker; and finally a data volume based approach where the customeris charged by the amount of data brought into the DFS for processing.

FIG. 34 is a flow diagram illustrating monitoring and metering servicesof the DFS system according to some embodiments of the presentdisclosure. In the illustrated embodiment, in step 3402, the DFSservices can monitor one or more metrics of a DFS system. The DFSservices can monitor the DFS system for a variety of reasons. Forexample, in step 3404, a DFS service can track metrics and/or displaymonitored metrics or data indicative of the monitored metrics. Hence,the metrics can be preselected by, for example, a system operator oradministrator seeking to analyze system stabilities, instabilities, orvulnerabilities.

In some embodiments, the DFS services can meter use of the DFS system asa mechanism for billing customers. For example, in step 3406, the DFSservices can monitor specific metrics for specific customers that usethe DFS system. The metering services can differ depending on whetherthe customer has a subscription to use the DFS system or is using theDFS system on an on-demand basis. As such, a DFS service can run avalue-based licensing agreement that allows customers to have a fairexchange of value for their use of the DFS service.

In step 3408, a determination is made about whether a customer has asubscription to use the DFS system. The subscription can define thescope of a license granted to a customer to access or use the DFSsystem. The scope can define an amount of functionality available to thecustomer. The functionality can include, for example, the number ortypes of searches that can be performed on the DFS system. In someembodiments, the scope granted to a user can vary in proportion to cost.For example, customers can purchase subscriptions of different scope fordifferent prices, depending on the needs of the customers. As such, aDFS service can run a value-based licensing agreement that allowscustomers to have a fair exchange of value for their use of the DFSservice.

In step 3410, if the customer is subscribed, the DFS service can metermetrics based on a subscription purchased by the customer. For example,a subscription to a DFS service may limit the amount of searches that acustomer can submitted to the DFS system. As such, the DFS service willmeter the number of searches that are submitted by the customer. Inanother example, a subscription to the DFS service may limit the time auser can actively access a DFS service. As such, the DFS service willmeter the amount of time that a user spends actively using the DFSservice.

In step 3412, a DFS service determine whether the customer's use of theDFS system exceeded a threshold amount granted by the subscription. Forexample, a customer may exceed the scope of a paid subscription by usingfunctionality not included in the paid subscription or using morefunctionality than that granted by the subscription. In someembodiments, the excess use can be measured with respect to a metricsuch as time or use of computing resources.

In step 3412, a DFS service determines whether a customer exceeded thescope of the customer's subscription. In step 3414, if the customer didnot exceed the subscription, no action is taken (e.g., the customer isnot charged additional fees). Referring back to step 3412, a variety ofactions can be taken if the customer has exceed the subscription. Instep 3416, the DFS service can charge the customer for the excess amountof the metered metric. For example, the DFS service may begin meteringthe amount of time a customer spends using the DFS system after athreshold amount of time has been exceeded. In step 3418, the DFSservice can alternatively or additionally prevent the customer fromaccessing the DFS system if the customer exceeds the subscription or hasnot paid the additional charges of step 3416.

Referring back to step 3408, if the customer is not subscribed to a DFSsubscription service, then customer may still access the DFS systemthrough a variety of other techniques. For example, a DFS service mayprovide limited or temporary access to the DFS system to anon-subscribed customer. In another example, a DFS service may provideaccess to the DFS service on-demand.

Either way, in step 3420, a DFS service meters metrics on anon-subscription basis. For example, in step 3422, the customer can payfor each instance the customer uses the DFS system. In another example,in step 3424, a DFS service can start charging a non-subscribed customerfor using the DFS system once the metrics of the service exceed athreshold amount. For example, a DFS service may provide free limitedaccess or temporary full access to the DFS system. When the measuringmetrics exceed the free limited access, the customer may be charged foraccess that exceeds the free amount. In either case, in step 3418, theDFS service can prevent the customer from accessing the DFS system ifthe measuring metrics exceed the threshold amount or the customer hasnot paid the charges of step 3422 or 3424. In some embodiments, a DFSserver can allow the customer to complete an active search that exceededa measuring metric but deny the customer from using the DFS system anyfurther until additional payment authorized.

11.0. Computing System Architecture

FIG. 35 is a block diagram illustrating a high-level example of ahardware architecture of a computing system in which an embodiment maybe implemented. For example, the hardware architecture of a computingsystem 72 can be used to implement any one or more of the functionalcomponents described herein (e.g., forwarder, indexer, search head, anddata store, server computer system, edge device). In some embodiments,one or multiple instances of the computing system 72 can be used toimplement the techniques described herein, where multiple such instancescan be coupled to each other via one or more networks.

The illustrated computing system 72 includes one or more processingdevices 74, one or more memory devices 76, one or more communicationdevices 78, one or more input/output (I/O) devices 80, and one or moremass storage devices 82, all coupled to each other through aninterconnect 84. The interconnect 84 may be or include one or moreconductive traces, buses, point-to-point connections, controllers,adapters, and/or other conventional connection devices. Each of theprocessing devices 74 controls, at least in part, the overall operationof the processing of the computing system 72 and can be or include, forexample, one or more general-purpose programmable microprocessors,digital signal processors (DSPs), mobile application processors,microcontrollers, application-specific integrated circuits (ASICs),programmable gate arrays (PGAs), or the like, or a combination of suchdevices.

Each of the memory devices 76 can be or include one or more physicalstorage devices, which may be in the form of random access memory (RAM),read-only memory (ROM) (which may be erasable and programmable), flashmemory, miniature hard disk drive, or other suitable type of storagedevice, or a combination of such devices. Each mass storage device 82can be or include one or more hard drives, digital versatile disks(DVDs), flash memories, or the like. Each memory device 76 and/or massstorage device 82 can store (individually or collectively) data andinstructions that configure the processing device(s) 74 to executeoperations to implement the techniques described above.

Each communication device 78 may be or include, for example, an Ethernetadapter, cable modem, Wi-Fi adapter, cellular transceiver, basebandprocessor, Bluetooth or Bluetooth Low Energy (BLE) transceiver, or thelike, or a combination thereof. Depending on the specific nature andpurpose of the processing devices 74, each I/O device 80 can be orinclude a device such as a display (which may be a touch screendisplay), audio speaker, keyboard, mouse or other pointing device,microphone, camera, etc. Note, however, that such I/O devices 80 may beunnecessary if the processing device 74 is embodied solely as a servercomputer.

In the case of a client device (e.g., edge device), the communicationdevices(s) 78 can be or include, for example, a cellulartelecommunications transceiver (e.g., 3G, LTE/4G, 5G), Wi-Fitransceiver, baseband processor, Bluetooth or BLE transceiver, or thelike, or a combination thereof. In the case of a server, thecommunication device(s) 78 can be or include, for example, any of theaforementioned types of communication devices, a wired Ethernet adapter,cable modem, DSL modem, or the like, or a combination of such devices.

A software program or algorithm, when referred to as “implemented in acomputer-readable storage medium,” includes computer-readableinstructions stored in a memory device (e.g., memory device(s) 76). Aprocessor (e.g., processing device(s) 74) is “configured to execute asoftware program” when at least one value associated with the softwareprogram is stored in a register that is readable by the processor. Insome embodiments, routines executed to implement the disclosedtechniques may be implemented as part of OS software (e.g., MICROSOFTWINDOWS® and LINUX®) or a specific software application, algorithmcomponent, program, object, module, or sequence of instructions referredto as “computer programs.”

Computer programs typically comprise one or more instructions set atvarious times in various memory devices of a computing device, which,when read and executed by at least one processor (e.g., processingdevice(s) 74), will cause a computing device to execute functionsinvolving the disclosed techniques. In some embodiments, a carriercontaining the aforementioned computer program product is provided. Thecarrier is one of an electronic signal, an optical signal, a radiosignal, or a non-transitory computer-readable storage medium (e.g., thememory device(s) 76).

Any or all of the features and functions described above can be combinedwith each other, except to the extent it may be otherwise stated aboveor to the extent that any such embodiments may be incompatible by virtueof their function or structure, as will be apparent to persons ofordinary skill in the art. Unless contrary to physical possibility, itis envisioned that (i) the methods/steps described herein may beperformed in any sequence and/or in any combination, and (ii) thecomponents of respective embodiments may be combined in any manner.

Although the subject matter has been described in language specific tostructural features and/or acts, it is to be understood that the subjectmatter defined in the appended claims is not necessarily limited to thespecific features or acts described above. Rather, the specific featuresand acts described above are disclosed as examples of implementing theclaims, and other equivalent features and acts are intended to be withinthe scope of the claims.

1.-30. (canceled)
 31. A method comprising: receiving, by a searchservice system in a shared computing resource environment, an indicationof a search query; determining, by the search service system, a type ofdistributed processing system to use for performing a search of aplurality of data sources based on a scope of the search query received,at least one data source of the plurality of data sources storing dataas a plurality of time-indexed events associated with a time-stamp andincluding a portion of raw machine data; dynamically configuringinstances of one or more components of the search service system in theshared computing resource environment using the determined type ofdistributed processing system based on the scope of the search queryreceived; obtaining, by the search service system, first partial searchresults from a first data storage system of the plurality of datasources; aggregating, by the search service system, the first partialsearch results obtained from the first data storage system with secondpartial search results obtained from a second data storage system of theplurality of data sources; and providing aggregated search results to asearch head associated with the search service system.
 32. The method ofclaim 31, wherein the search head causes output of the aggregated searchresults or data indicative of the aggregated search results by a displaydevice to a user.
 33. The method of claim 32, wherein the aggregatesearch results are displayed as a timeline visualization.
 34. The methodof claim 31, wherein the first partial search results include aplurality of time-indexed events including portions of raw machine data.35. The method of claim 31, wherein the second partial search resultscomprise data in a structured format.
 36. The method of claim 31,wherein the plurality of data sources includes a cloud storage systemand an external data storage system, wherein the cloud storage systemstores data as a plurality of time-indexed events including portions ofraw machine data.
 37. The method of claim 31, wherein the first partialsearch results are in a first format, the second partial search resultsare in a second format different than the first format, and theaggregate search results are in a specified format.
 38. The method ofclaim 31, wherein the obtaining is by the instances of the one or morecomponents of the search service system.
 39. The method of claim 31,wherein a single cluster of the distributed processing system is usedfor all search operations.
 40. The method of claim 31, wherein aplurality of clusters of the distributed processing system of differingscale types are used for differing types of search requests.
 41. Themethod of claim 40, wherein the plurality of clusters of the distributedprocessing system include a first cluster type used for searchaggregation processing involving full search of the plurality of datasources and a second cluster type used for partial aggregationprocessing of partial search results from different data sources. 42.The method of claim 41, wherein a scale of a cluster for the firstcluster type or the second cluster type is set for each search query bya user.
 43. The method of claim 41, wherein a scale of a cluster for thefirst cluster type or the second cluster type is based, at least inpart, on a role quota.
 44. The method of claim 43, wherein the scale ofthe cluster is based, at least in part, on information regarding a userthat submitted the search query.
 45. The method of claim 31, whereindynamically configuring the instances of the one or more components ofthe search service system comprises configuring the search head tocontrol processing of the search query.
 46. The method of claim 31,wherein dynamically configuring the instances of the one or morecomponents of the search service system comprises configuring a cloudworker node to obtain the first partial search results.
 47. The methodof claim 31, wherein the aggregated search results are computed andreported at scale to the search head.
 48. The method of claim 47,further comprising reducing the aggregated search results from theplurality of data sources at scale before providing the aggregatedsearch results to the search head.
 49. The method of claim 31, furthercomprising collecting metrics for a heuristic determination of spikes inrequirements of the search service system for the search.
 50. The methodof claim 49, wherein the heuristic determination is accelerated throughauto-scaling.
 51. The method of claim 31, further comprising replicatingapplications of the search service system over a cluster of thedistributed processing system to ensure that the applications areexecuted at scale.
 52. The method of claim 31, establishing user-quotaor role-quota levels based, at least in part, on synchronization betweenuser management features of the search service system and access controlfeatures of the distributed processing system.
 53. The method of claim31, wherein the search query is input by a user and expressed in apipelined search language.
 54. The method of claim 31, furthercomprising metering an amount of time and computing resources utilizedto complete the search query.
 55. A system comprising: a memory; and aprocessing device in a shared computing resource environment and coupledwith the memory to: receive an indication of a search query; determine atype of distributed processing system to use for performing a search ofa plurality of data sources based on a scope of the search queryreceived, at least one data source of the plurality of data sourcesstoring data as a plurality of time-indexed events associated with atime-stamp and including a portion of raw machine data; dynamicallyconfigure instances of one or more components of a search service systemin the shared computing resource environment using the determined typeof distributed processing system based on the scope of the search queryreceived; obtain first partial search results from a first data storagesystem of the plurality of data sources; aggregating the first partialsearch results obtained from the first data storage system with secondpartial search results obtained from a second data storage system of theplurality of data sources; and provide aggregated search results to asearch head.
 56. The system of claim 55, wherein the aggregated searchresults include a plurality of time-indexed events including portions ofraw machine data.
 57. The system of claim 55, wherein the plurality ofdata sources includes a cloud storage system and an external datastorage system, wherein the cloud storage system stores data as aplurality of time-indexed events including portions of raw machine data.58. A non-transitory computer-readable medium encoding instructionsthereon that, in response to execution by one or more processing devicesin a shared computing resource environment, cause the one or moreprocessing devices to perform operations comprising: receiving anindication of a search query; determining a type of distributedprocessing system to use for performing a search of a plurality of datasources based on a scope of the search query received, at least one datasource of the plurality of data sources storing data as a plurality oftime-indexed events associated with a time-stamp and including a portionof raw machine data; dynamically configuring instances of one or morecomponents of a search service system in the shared computing resourceenvironment using the determined type of distributed processing systembased on the scope of the search query received; obtaining first partialsearch results from a first data storage system of the plurality of datasources; aggregating the first partial search results obtained from thefirst data storage system with second partial search results obtainedfrom a second data storage system of the plurality of data sources; andproviding aggregated search results to a search head.
 59. Thenon-transitory computer-readable medium of claim 28, wherein theaggregated search results include a plurality of time-indexed eventsincluding portions of raw machine data.
 60. The non-transitorycomputer-readable medium of claim 28, wherein the plurality of datasources includes a cloud storage system and an external data storagesystem, wherein the cloud storage system stores data as a plurality oftime-indexed events including portions of raw machine data.